The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications...The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style transfer.Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image.CYCLE-GAN is a classic GAN model,which has a wide range of scenarios in style transfer.Considering its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output image.However,it is difficult for CYCLE-GAN to converge and generate high-quality images.In order to solve this problem,spectral normalization is introduced into each convolutional kernel of the discriminator.Every convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed model.Besides,we use pretrained model(VGG16)to control the loss of image content in the position of l1 regularization.To avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss function.In terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative features.Experimental results show that the proposed model converges faster and achieves better FID scores than the state of the art.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs.However,humans naturally use speech for visualization prompts.Therefore,this paper...The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs.However,humans naturally use speech for visualization prompts.Therefore,this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks(GANs)model,leveraging Speech-to-Text translation along with the CLIP+VQGAN model.The proposed method involves translating speech prompts into text,which is then used by the Contrastive Language-Image Pretraining(CLIP)+Vector Quantized Generative Adversarial Network(VQGAN)model to generate images.This paper outlines the steps required to implement such a model and describes in detail the methods used for evaluating the model.The GAN model successfully generates artwork from descriptions using speech and text prompts.Experimental outcomes of synthesized images demonstrate that the proposed methodology can produce beautiful abstract visuals containing elements from the input prompts.The model achieved a Frechet Inception Distance(FID)score of 28.75,showcasing its capability to produce high-quality and diverse images.The proposed model can find numerous applications in educational,artistic,and design spaces due to its ability to generate images using speech and the distinct abstract artistry of the output images.This capability is demonstrated by giving the model out-of-the-box prompts to generate never-before-seen images with plausible realistic qualities.展开更多
Symmetric encryption algorithms learned by the previous proposed end-to-end adversarial network encryption communication systems are deterministic.With the same key and same plaintext,the deterministic algorithm will ...Symmetric encryption algorithms learned by the previous proposed end-to-end adversarial network encryption communication systems are deterministic.With the same key and same plaintext,the deterministic algorithm will lead to the same ciphertext.This means that the key in the deterministic encryption algorithm can only be used once,thus the encryption is not practical.To solve this problem,a nondeterministic symmetric encryption end-to-end communication system based on generative adversarial networks is proposed.We design a nonce-based adversarial neural network model,where a“nonce”standing for“number used only once”is passed to communication participants,and does not need to be secret.Moreover,we optimize the network structure through adding Batch Normalization(BN)to the CNNs(Convolutional Neural Networks),selecting the appropriate activation functions,and setting appropriate CNNs parameters.Results of experiments and analysis show that our system can achieve non-deterministic symmetric encryption,where Alice encrypting the same plaintext with the key twice will generate different ciphertexts,and Bob can decrypt all these different ciphertexts of the same plaintext to the correct plaintext.And our proposed system has fast convergence and the correct rate of decryption when the plaintext length is 256 or even longer.展开更多
An efficient,diversified,and low-dimensional airfoil parameterization method is critical to airfoil aerodynamic optimization design.This paper proposes a supersonic airfoil parameterization method based on a bijective...An efficient,diversified,and low-dimensional airfoil parameterization method is critical to airfoil aerodynamic optimization design.This paper proposes a supersonic airfoil parameterization method based on a bijective cycle generative adversarial network(Bicycle-GAN),whose performance is compared with that of the conditional variational autoencoder(cVAE)based parameterization method in terms of parsimony,flawlessness,intuitiveness,and physicality.In all four aspects,the Bicycle-GAN-based parameterization method is superior to the cVAEbased parameterization method.Combined with multifidelity Gaussian process regression(MFGPR)surrogate model and a Bayesian optimization algorithm,a Bicycle-GAN-based optimization framework is established for the aerodynamic performance optimization of airfoils immersed in supersonic flow,which is compared with the cVAE-based optimization method in terms of optimized efficiency and effectiveness.The MFGPR surrogate model is established using low-fidelity aerodynamic data obtained from supersonic thin-airfoil theory and high-fidelity aerodynamic data obtained from steady CFD simulation.For both supersonic conditions,the CFD simulation costs are reduced by>20%compared with those of the cVAE-based optimization,and better optimization results are obtained through the Bicycle-GAN model.The optimization results for this supersonic flow point to a sharper leading edge,a smaller camber and thickness with a flatter lower surface,and a maximum thickness at 50%chord length.The advantages of the Bicycle-GAN and MFGPR models are comprehensively demonstrated in terms of airfoil generation characteristics,surrogate model prediction accuracy and optimization efficiency.展开更多
The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in num...The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in numerical simulations may lead to significantinaccuracies.In this paper,we present a novel intelligence framework based on a deep convolutional generative adversarial network(DCGAN).A DCGAN model was trained using a training dataset comprising 11,625 real particles for the random generation of three-dimensional calcareous sand particles.Subsequently,3800 realistic calcareous sand particles with intra-particle voids were generated.Generative fidelityand validity of the DCGAN model were well verifiedby the consistency of the statistical values of nine morphological parameters of both the training dataset and the generated dataset.Digital calcareous sand columns were obtained through gravitational deposition simulation of the generated particles.Directional seepage simulations were conducted,and the vertical permeability values of the sand columns were found to be in accordance with the objective law.The results demonstrate the potential of the proposed framework for stochastic modeling and multi-scale simulation of the seepage behaviors in calcareous sand foundations and backfills.展开更多
An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extra...An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction,which are commonly faced by rolling bearings and lead to low diagnostic accuracy.Initially,dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty(1D-2DWDCGAN)are constructed to augment the original dataset.A self-adaptive loss threshold control training strategy is introduced,and establishing a self-adaptive balancing mechanism for stable model training.Subsequently,a diagnostic model based on multidimensional feature fusion is designed,wherein complex features from various dimensions are extracted,merging the original signal waveform features,structured features,and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales;thus,efficient and accurate small sample fault diagnosis is facilitated.Finally,an experiment between the bearing fault dataset of CaseWestern ReserveUniversity and the fault simulation experimental platformdataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy.The diagnostic accuracy after data augmentation reached 99.94%and 99.87%in two different experimental environments,respectively.In addition,robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds,verifying its good generalization performance.展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adver...Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea.The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution.Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application fields.Then, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.展开更多
This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models.It can reproduce a wide range of conceptual geological models while possessing the fle...This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models.It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data.Compared with existing geostatistics-based modeling methods,our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks(GANs).GANs couple a generator with a discriminator,and each uses a deep convolutional neural network.The networks are trained in an adversarial manner until the generator can create "fake" images that the discriminator cannot distinguish from "real" images.We extend the original GAN approach to 3D geological modeling at the reservoir scale.The GANs are trained using a library of 3D facies models.Once the GANs have been trained,they can generate a variety of geologically realistic facies models constrained by well data interpretations.This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends.The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods,which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.展开更多
Multifocal metalenses are of great concern in optical communications,optical imaging and micro-optics systems,but their design is extremely challenging.In recent years,deep learning methods have provided novel solutio...Multifocal metalenses are of great concern in optical communications,optical imaging and micro-optics systems,but their design is extremely challenging.In recent years,deep learning methods have provided novel solutions to the design of optical planar devices.Here,an approach is proposed to explore the use of generative adversarial networks(GANs)to realize the design of metalenses with different focusing positions at dual wavelengths.This approach includes a forward network and an inverse network,where the former predicts the optical response of meta-atoms and the latter generates structures that meet specific requirements.Compared to the traditional search method,the inverse network demonstrates higher precision and efficiency in designing a dual-wavelength bifocal metalens.The results will provide insights and methodologies for the design of tunable wavelength metalenses,while also highlighting the potential of deep learning in optical device design.展开更多
Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats.Attackers can non-invasively manipulate sensors and spoof controllers,which in tur...Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats.Attackers can non-invasively manipulate sensors and spoof controllers,which in turn increases the autonomy of the system.Even though the focus on protecting against sensor attacks increases,there is still uncertainty about the optimal timing for attack detection.Existing systems often struggle to manage the trade-off between latency and false alarm rate,leading to inefficiencies in real-time anomaly detection.This paper presents a framework designed to monitor,predict,and control dynamic systems with a particular emphasis on detecting and adapting to changes,including anomalies such as“drift”and“attack”.The proposed algorithm integrates a Transformer-based Attention Generative Adversarial Residual model,which combines the strengths of generative adversarial networks,residual networks,and attention algorithms.The system operates in two phases:offline and online.During the offline phase,the proposed model is trained to learn complex patterns,enabling robust anomaly detection.The online phase applies a trained model,where the drift adapter adjusts the model to handle data changes,and the attack detector identifies deviations by comparing predicted and actual values.Based on the output of the attack detector,the controller makes decisions then the actuator executes suitable actions.Finally,the experimental findings show that the proposed model balances detection accuracy of 99.25%,precision of 98.84%,sensitivity of 99.10%,specificity of 98.81%,and an F1-score of 98.96%,thus provides an effective solution for dynamic and safety-critical environments.展开更多
Drug-drug interaction(DDI)refers to the interaction between two or more drugs in the body,altering their efficacy or pharmacokinetics.Fully considering and accurately predicting DDI has become an indispensable part of...Drug-drug interaction(DDI)refers to the interaction between two or more drugs in the body,altering their efficacy or pharmacokinetics.Fully considering and accurately predicting DDI has become an indispensable part of ensuring safe medication for patients.In recent years,many deep learning-based methods have been proposed to predict DDI.However,most existing computational models tend to oversimplify the fusion of drug structural and topological information,often relying on methods such as splicing or weighted summation,which fail to adequately capture the potential complementarity between structural and topological features.This loss of information may lead to models that do not fully leverage these features,thus limiting their performance in DDI prediction.To address these challenges,we propose a relation-aware cross adversarial network for predicting DDI,named RCAN-DDI,which combines a relationship-aware structure feature learning module and a topological feature learning module based on DDI networks to capture multimodal features of drugs.To explore the correlations and complementarities among different information sources,the cross-adversarial network is introduced to fully integrate features from various modalities,enhancing the predictive performance of the model.The experimental results demonstrate that the RCAN-DDI method outperforms other methods.Even in cases of labelled DDI scarcity,the method exhibits good robustness in the DDI prediction task.Furthermore,the effectiveness of the cross-adversarial module is validated through ablation experiments,demonstrating its superiority in learning multimodal complementary information.展开更多
Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive...Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive framework based on generative adversarial network(GAN)for characterizing pore structure properties of shale,which incorporates image augmentation,super-resolution reconstruction,and multi-mineral auto-segmentation.Using real 2D and 3D shale images,the framework was assessed through correlation function,entropy,porosity,pore size distribution,and permeability.The application results show that this framework enables the enhancement of 3D low-resolution digital cores by a scale factor of 8,without paired shale images,effectively reconstructing the unresolved fine-scale pores under a low resolution,rather than merely denoising,deblurring,and edge clarification.The trained GAN-based segmentation model effectively improves manual multi-mineral segmentation results,resulting in a strong resemblance to real samples in terms of pore size distribution and permeability.This framework significantly improves the characterization of complex shale microstructures and can be expanded to other heterogeneous porous media,such as carbonate,coal,and tight sandstone reservoirs.展开更多
Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process...Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.展开更多
This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,...This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,particularly suited for regions with limited seismic data.Herein,we report a generative adversarial network(GAN)framework capable of simulating strong ground motions under various environmental conditions using only a small set of real earthquake records.The constructed GAN model generates ground motions based on continuous physical variables such as source distance,site conditions,and magnitude,effectively capturing the complexity and diversity of ground motions under different scenarios.This capability allows the proposed model to approximate real seismic data,making it applicable to a wide range of engineering purposes.Using the Shandong Pingyuan earthquake as an example,a specialized dataset was constructed based on regional real ground motion records.The response spectrum at target locations was obtained through inverse distance-weighted interpolation of actual response spectra,followed by continuous wavelet transform to derive the ground motion time histories at these locations.Through iterative parameter adjustments,the constructed GAN model learned the probability distribution of strong-motion data for this event.The trained model generated three-component ground-motion time histories with clear P-wave and S-wave characteristics,accurately reflecting the non-stationary nature of seismic records.Statistical comparisons between synthetic and real response spectra,waveform envelopes,and peak ground acceleration show a high degree of similarity,underscoring the effectiveness of the model in replicating both the statistical and physical characteristics of real ground motions.These findings validate the feasibility of GANs for generating realistic earthquake data in data-scarce regions,providing a reliable approach for enriching regional ground motion databases.Additionally,the results suggest that GAN-based networks are a powerful tool for building predictive models in seismic hazard analysis.展开更多
In the field of imaging,the image resolution is required to be higher.There is always a contradiction between the sensitivity and resolution of the seeker in the infrared guidance system.This work uses the rosette sca...In the field of imaging,the image resolution is required to be higher.There is always a contradiction between the sensitivity and resolution of the seeker in the infrared guidance system.This work uses the rosette scanning mode for physical compression imaging in order to improve the resolution of the image as much as possible under the high-sensitivity infrared rosette point scanning mode and complete the missing information that is not scanned.It is effective to use optical lens instead of traditional optical reflection system,which can reduce the loss in optical path transmission.At the same time,deep learning neural network is used for control.An infrared single pixel imaging system that integrates sparse algorithm and recovery algorithm through the improved generative adversarial networks is trained.The experiment on the infrared aerial target dataset shows that when the input is sparse image after rose sampling,the system finally can realize the single pixel recovery imaging of the infrared image,which improves the resolution of the image while ensuring high sensitivity.展开更多
The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(...The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.展开更多
Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose a...Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose an Attention Spatio-Temporal predictive Generative Adversarial Network(AST-GAN)model for predicting the temporal and spatial distribution of TCs.The model forecasts the spatial distribution of TC wind speeds for the next 15 hours at 3-hour intervals,emphasizing the cyclone's center,high wind-speed areas,and its asymmetric structure.To effectively capture spatiotemporal feature transfer at different time steps,we employ a channel attention mechanism for feature selection,enhancing model performance and reducing parameter redundancy.We utilized High-Resolution Weather Research and Forecasting(HWRF)data to train our model,allowing it to assimilate a wide range of TC motion patterns.The model is versatile and can be applied to various complex scenarios,such as multiple TCs moving simultaneously or TCs approaching landfall.Our proposed model demonstrates superior forecasting performance,achieving a root-mean-square error(RMSE)of 0.71 m s^(-1)for overall wind speed and 2.74 m s^(-1)for maximum wind speed when benchmarked against ground truth data from HWRF.Furthermore,the model underwent optimization and independent testing using ERA5reanalysis data,showcasing its stability and scalability.After fine-tuning on the ERA5 dataset,the model achieved an RMSE of 1.33 m s^(-1)for wind speed and 1.75 m s^(-1)for maximum wind speed.The AST-GAN model outperforms other state-of-the-art models in RMSE on both the HWRF and ERA5 datasets,maintaining its superior performance and demonstrating its effectiveness for spatiotemporal prediction of TCs.展开更多
基金This work is supported by the National Natural Science Foundation of China(No.61702226)the 111 Project(B12018)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20170200)the Fundamental Research Funds for the Central Universities(No.JUSRP11854).
文摘The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style transfer.Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image.CYCLE-GAN is a classic GAN model,which has a wide range of scenarios in style transfer.Considering its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output image.However,it is difficult for CYCLE-GAN to converge and generate high-quality images.In order to solve this problem,spectral normalization is introduced into each convolutional kernel of the discriminator.Every convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed model.Besides,we use pretrained model(VGG16)to control the loss of image content in the position of l1 regularization.To avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss function.In terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative features.Experimental results show that the proposed model converges faster and achieves better FID scores than the state of the art.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
基金funded by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology SydneyMoreover,supported by the Researchers Supporting Project,King Saud University,Riyadh,Saudi Arabia,under Ongoing Research Funding(ORF-2025-14).
文摘The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs.However,humans naturally use speech for visualization prompts.Therefore,this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks(GANs)model,leveraging Speech-to-Text translation along with the CLIP+VQGAN model.The proposed method involves translating speech prompts into text,which is then used by the Contrastive Language-Image Pretraining(CLIP)+Vector Quantized Generative Adversarial Network(VQGAN)model to generate images.This paper outlines the steps required to implement such a model and describes in detail the methods used for evaluating the model.The GAN model successfully generates artwork from descriptions using speech and text prompts.Experimental outcomes of synthesized images demonstrate that the proposed methodology can produce beautiful abstract visuals containing elements from the input prompts.The model achieved a Frechet Inception Distance(FID)score of 28.75,showcasing its capability to produce high-quality and diverse images.The proposed model can find numerous applications in educational,artistic,and design spaces due to its ability to generate images using speech and the distinct abstract artistry of the output images.This capability is demonstrated by giving the model out-of-the-box prompts to generate never-before-seen images with plausible realistic qualities.
基金supported by The National Defense Innovation Project(No.ZZKY20222411)Natural Science Basic Research Plan in Shaanxi Province of China(No.2024JC-YBMS-546).
文摘Symmetric encryption algorithms learned by the previous proposed end-to-end adversarial network encryption communication systems are deterministic.With the same key and same plaintext,the deterministic algorithm will lead to the same ciphertext.This means that the key in the deterministic encryption algorithm can only be used once,thus the encryption is not practical.To solve this problem,a nondeterministic symmetric encryption end-to-end communication system based on generative adversarial networks is proposed.We design a nonce-based adversarial neural network model,where a“nonce”standing for“number used only once”is passed to communication participants,and does not need to be secret.Moreover,we optimize the network structure through adding Batch Normalization(BN)to the CNNs(Convolutional Neural Networks),selecting the appropriate activation functions,and setting appropriate CNNs parameters.Results of experiments and analysis show that our system can achieve non-deterministic symmetric encryption,where Alice encrypting the same plaintext with the key twice will generate different ciphertexts,and Bob can decrypt all these different ciphertexts of the same plaintext to the correct plaintext.And our proposed system has fast convergence and the correct rate of decryption when the plaintext length is 256 or even longer.
基金supported by the National Natural Science Founda-tion of China(Grant No 12302226)the China Postdoctoral Science Foundation(Grant No BX20230453).
文摘An efficient,diversified,and low-dimensional airfoil parameterization method is critical to airfoil aerodynamic optimization design.This paper proposes a supersonic airfoil parameterization method based on a bijective cycle generative adversarial network(Bicycle-GAN),whose performance is compared with that of the conditional variational autoencoder(cVAE)based parameterization method in terms of parsimony,flawlessness,intuitiveness,and physicality.In all four aspects,the Bicycle-GAN-based parameterization method is superior to the cVAEbased parameterization method.Combined with multifidelity Gaussian process regression(MFGPR)surrogate model and a Bayesian optimization algorithm,a Bicycle-GAN-based optimization framework is established for the aerodynamic performance optimization of airfoils immersed in supersonic flow,which is compared with the cVAE-based optimization method in terms of optimized efficiency and effectiveness.The MFGPR surrogate model is established using low-fidelity aerodynamic data obtained from supersonic thin-airfoil theory and high-fidelity aerodynamic data obtained from steady CFD simulation.For both supersonic conditions,the CFD simulation costs are reduced by>20%compared with those of the cVAE-based optimization,and better optimization results are obtained through the Bicycle-GAN model.The optimization results for this supersonic flow point to a sharper leading edge,a smaller camber and thickness with a flatter lower surface,and a maximum thickness at 50%chord length.The advantages of the Bicycle-GAN and MFGPR models are comprehensively demonstrated in terms of airfoil generation characteristics,surrogate model prediction accuracy and optimization efficiency.
基金financially supported by the National Natural Science Foundation of China(Grant No.42077232)the National Natural Science Foundation for Excellent Young Scholars of China(Grant No.52222110)the Fundamental Research Funds for the Central Universities(Grant No.14380229).
文摘The issues of seepage in calcareous sand foundations and backfillshave a potentially detrimental effect on the stability and safety of superstructures.Simplifying calcareous sand grains as spheres or ellipsoids in numerical simulations may lead to significantinaccuracies.In this paper,we present a novel intelligence framework based on a deep convolutional generative adversarial network(DCGAN).A DCGAN model was trained using a training dataset comprising 11,625 real particles for the random generation of three-dimensional calcareous sand particles.Subsequently,3800 realistic calcareous sand particles with intra-particle voids were generated.Generative fidelityand validity of the DCGAN model were well verifiedby the consistency of the statistical values of nine morphological parameters of both the training dataset and the generated dataset.Digital calcareous sand columns were obtained through gravitational deposition simulation of the generated particles.Directional seepage simulations were conducted,and the vertical permeability values of the sand columns were found to be in accordance with the objective law.The results demonstrate the potential of the proposed framework for stochastic modeling and multi-scale simulation of the seepage behaviors in calcareous sand foundations and backfills.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272259 and 52005148).
文摘An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction,which are commonly faced by rolling bearings and lead to low diagnostic accuracy.Initially,dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty(1D-2DWDCGAN)are constructed to augment the original dataset.A self-adaptive loss threshold control training strategy is introduced,and establishing a self-adaptive balancing mechanism for stable model training.Subsequently,a diagnostic model based on multidimensional feature fusion is designed,wherein complex features from various dimensions are extracted,merging the original signal waveform features,structured features,and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales;thus,efficient and accurate small sample fault diagnosis is facilitated.Finally,an experiment between the bearing fault dataset of CaseWestern ReserveUniversity and the fault simulation experimental platformdataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy.The diagnostic accuracy after data augmentation reached 99.94%and 99.87%in two different experimental environments,respectively.In addition,robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds,verifying its good generalization performance.
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
基金supported by the National Natural Science Foundation of China(61533019,71232006,91520301)
文摘Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea.The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution.Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application fields.Then, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.
文摘This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models.It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data.Compared with existing geostatistics-based modeling methods,our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks(GANs).GANs couple a generator with a discriminator,and each uses a deep convolutional neural network.The networks are trained in an adversarial manner until the generator can create "fake" images that the discriminator cannot distinguish from "real" images.We extend the original GAN approach to 3D geological modeling at the reservoir scale.The GANs are trained using a library of 3D facies models.Once the GANs have been trained,they can generate a variety of geologically realistic facies models constrained by well data interpretations.This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends.The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods,which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.
基金National Natural Science Foundation of China(No.61975029)。
文摘Multifocal metalenses are of great concern in optical communications,optical imaging and micro-optics systems,but their design is extremely challenging.In recent years,deep learning methods have provided novel solutions to the design of optical planar devices.Here,an approach is proposed to explore the use of generative adversarial networks(GANs)to realize the design of metalenses with different focusing positions at dual wavelengths.This approach includes a forward network and an inverse network,where the former predicts the optical response of meta-atoms and the latter generates structures that meet specific requirements.Compared to the traditional search method,the inverse network demonstrates higher precision and efficiency in designing a dual-wavelength bifocal metalens.The results will provide insights and methodologies for the design of tunable wavelength metalenses,while also highlighting the potential of deep learning in optical device design.
文摘Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats.Attackers can non-invasively manipulate sensors and spoof controllers,which in turn increases the autonomy of the system.Even though the focus on protecting against sensor attacks increases,there is still uncertainty about the optimal timing for attack detection.Existing systems often struggle to manage the trade-off between latency and false alarm rate,leading to inefficiencies in real-time anomaly detection.This paper presents a framework designed to monitor,predict,and control dynamic systems with a particular emphasis on detecting and adapting to changes,including anomalies such as“drift”and“attack”.The proposed algorithm integrates a Transformer-based Attention Generative Adversarial Residual model,which combines the strengths of generative adversarial networks,residual networks,and attention algorithms.The system operates in two phases:offline and online.During the offline phase,the proposed model is trained to learn complex patterns,enabling robust anomaly detection.The online phase applies a trained model,where the drift adapter adjusts the model to handle data changes,and the attack detector identifies deviations by comparing predicted and actual values.Based on the output of the attack detector,the controller makes decisions then the actuator executes suitable actions.Finally,the experimental findings show that the proposed model balances detection accuracy of 99.25%,precision of 98.84%,sensitivity of 99.10%,specificity of 98.81%,and an F1-score of 98.96%,thus provides an effective solution for dynamic and safety-critical environments.
基金supported by the Natural Science Foundation of Shandong Province(Grant No.:ZR2023MF053)the National Natural Science Foundation of China(Grant No.:61902430).
文摘Drug-drug interaction(DDI)refers to the interaction between two or more drugs in the body,altering their efficacy or pharmacokinetics.Fully considering and accurately predicting DDI has become an indispensable part of ensuring safe medication for patients.In recent years,many deep learning-based methods have been proposed to predict DDI.However,most existing computational models tend to oversimplify the fusion of drug structural and topological information,often relying on methods such as splicing or weighted summation,which fail to adequately capture the potential complementarity between structural and topological features.This loss of information may lead to models that do not fully leverage these features,thus limiting their performance in DDI prediction.To address these challenges,we propose a relation-aware cross adversarial network for predicting DDI,named RCAN-DDI,which combines a relationship-aware structure feature learning module and a topological feature learning module based on DDI networks to capture multimodal features of drugs.To explore the correlations and complementarities among different information sources,the cross-adversarial network is introduced to fully integrate features from various modalities,enhancing the predictive performance of the model.The experimental results demonstrate that the RCAN-DDI method outperforms other methods.Even in cases of labelled DDI scarcity,the method exhibits good robustness in the DDI prediction task.Furthermore,the effectiveness of the cross-adversarial module is validated through ablation experiments,demonstrating its superiority in learning multimodal complementary information.
基金Supported by the National Natural Science Foundation of China(U23A20595,52034010,52288101)National Key Research and Development Program of China(2022YFE0203400)+1 种基金Shandong Provincial Natural Science Foundation(ZR2024ZD17)Fundamental Research Funds for the Central Universities(23CX10004A).
文摘Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive framework based on generative adversarial network(GAN)for characterizing pore structure properties of shale,which incorporates image augmentation,super-resolution reconstruction,and multi-mineral auto-segmentation.Using real 2D and 3D shale images,the framework was assessed through correlation function,entropy,porosity,pore size distribution,and permeability.The application results show that this framework enables the enhancement of 3D low-resolution digital cores by a scale factor of 8,without paired shale images,effectively reconstructing the unresolved fine-scale pores under a low resolution,rather than merely denoising,deblurring,and edge clarification.The trained GAN-based segmentation model effectively improves manual multi-mineral segmentation results,resulting in a strong resemblance to real samples in terms of pore size distribution and permeability.This framework significantly improves the characterization of complex shale microstructures and can be expanded to other heterogeneous porous media,such as carbonate,coal,and tight sandstone reservoirs.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12122403 and 12327807).
文摘Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.
基金Funded by the National Key Research and Development Program(2022YFC3003502).
文摘This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,particularly suited for regions with limited seismic data.Herein,we report a generative adversarial network(GAN)framework capable of simulating strong ground motions under various environmental conditions using only a small set of real earthquake records.The constructed GAN model generates ground motions based on continuous physical variables such as source distance,site conditions,and magnitude,effectively capturing the complexity and diversity of ground motions under different scenarios.This capability allows the proposed model to approximate real seismic data,making it applicable to a wide range of engineering purposes.Using the Shandong Pingyuan earthquake as an example,a specialized dataset was constructed based on regional real ground motion records.The response spectrum at target locations was obtained through inverse distance-weighted interpolation of actual response spectra,followed by continuous wavelet transform to derive the ground motion time histories at these locations.Through iterative parameter adjustments,the constructed GAN model learned the probability distribution of strong-motion data for this event.The trained model generated three-component ground-motion time histories with clear P-wave and S-wave characteristics,accurately reflecting the non-stationary nature of seismic records.Statistical comparisons between synthetic and real response spectra,waveform envelopes,and peak ground acceleration show a high degree of similarity,underscoring the effectiveness of the model in replicating both the statistical and physical characteristics of real ground motions.These findings validate the feasibility of GANs for generating realistic earthquake data in data-scarce regions,providing a reliable approach for enriching regional ground motion databases.Additionally,the results suggest that GAN-based networks are a powerful tool for building predictive models in seismic hazard analysis.
基金the Fundamental Research Funds for the Central Universities(No.3072022CF0802)。
文摘In the field of imaging,the image resolution is required to be higher.There is always a contradiction between the sensitivity and resolution of the seeker in the infrared guidance system.This work uses the rosette scanning mode for physical compression imaging in order to improve the resolution of the image as much as possible under the high-sensitivity infrared rosette point scanning mode and complete the missing information that is not scanned.It is effective to use optical lens instead of traditional optical reflection system,which can reduce the loss in optical path transmission.At the same time,deep learning neural network is used for control.An infrared single pixel imaging system that integrates sparse algorithm and recovery algorithm through the improved generative adversarial networks is trained.The experiment on the infrared aerial target dataset shows that when the input is sparse image after rose sampling,the system finally can realize the single pixel recovery imaging of the infrared image,which improves the resolution of the image while ensuring high sensitivity.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549).
文摘The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.
基金supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(NO.SML2021SP201)the National Natural Science Foundation of China(Grant No.42306200 and 42306216)+2 种基金the National Key Research and Development Program of China(Grant No.2023YFC3008100)the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.311021004)the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(Project No.SL2021ZD203)。
文摘Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose an Attention Spatio-Temporal predictive Generative Adversarial Network(AST-GAN)model for predicting the temporal and spatial distribution of TCs.The model forecasts the spatial distribution of TC wind speeds for the next 15 hours at 3-hour intervals,emphasizing the cyclone's center,high wind-speed areas,and its asymmetric structure.To effectively capture spatiotemporal feature transfer at different time steps,we employ a channel attention mechanism for feature selection,enhancing model performance and reducing parameter redundancy.We utilized High-Resolution Weather Research and Forecasting(HWRF)data to train our model,allowing it to assimilate a wide range of TC motion patterns.The model is versatile and can be applied to various complex scenarios,such as multiple TCs moving simultaneously or TCs approaching landfall.Our proposed model demonstrates superior forecasting performance,achieving a root-mean-square error(RMSE)of 0.71 m s^(-1)for overall wind speed and 2.74 m s^(-1)for maximum wind speed when benchmarked against ground truth data from HWRF.Furthermore,the model underwent optimization and independent testing using ERA5reanalysis data,showcasing its stability and scalability.After fine-tuning on the ERA5 dataset,the model achieved an RMSE of 1.33 m s^(-1)for wind speed and 1.75 m s^(-1)for maximum wind speed.The AST-GAN model outperforms other state-of-the-art models in RMSE on both the HWRF and ERA5 datasets,maintaining its superior performance and demonstrating its effectiveness for spatiotemporal prediction of TCs.
