Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
The applications of machine learning(ML)in the medical domain are often hindered by the limited availability of high-quality data.To address this challenge,we explore the synthetic generation of echocardiography image...The applications of machine learning(ML)in the medical domain are often hindered by the limited availability of high-quality data.To address this challenge,we explore the synthetic generation of echocardiography images(echoCG)using state-of-the-art generative models.We conduct a comprehensive evaluation of three prominent methods:Cycle-consistent generative adversarial network(CycleGAN),Contrastive Unpaired Translation(CUT),and Stable Diffusion 1.5 with Low-Rank Adaptation(LoRA).Our research presents the data generation methodol-ogy,image samples,and evaluation strategy,followed by an extensive user study involving licensed cardiologists and surgeons who assess the perceived quality and medical soundness of the generated images.Our findings indicate that Stable Diffusion outperforms both CycleGAN and CUT in generating images that are nearly indistinguishable from real echoCG images,making it a promising tool for augmenting medical datasets.However,we also identify limitations in the synthetic images generated by CycleGAN and CUT,which are easily distinguishable as non-realistic by medical professionals.This study highlights the potential of diffusion models in medical imaging and their applicability in addressing data scarcity,while also outlining the areas for future improvement.展开更多
Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act ...Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.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
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
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
In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and th...In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.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
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
The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio m...The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio map serves as a crucial tool for describing signal strength distribution within the current electromagnetic environment.However,most existing algorithms rely on sparse measurements of radio strength,disregarding the impact of building information.In this paper,we propose a spectrum cartography(SC)algorithm that eliminates the need for relying on sparse ground-based radio strength measurements by utilizing a satellite network to collect data on buildings and transmitters.Our algorithm leverages Pix2Pix Generative Adversarial Network(GAN)to construct accurate radio maps using transmitter information within real geographical environments.Finally,simulation results demonstrate that our algorithm exhibits superior accuracy compared to previously proposed methods.展开更多
Background:Enhancing the quality of images from retinal,MRI and echocardiography imaging shows promise with SR-GANs for medical imaging use.Using these networks,it is possible to produce high-quality images even from ...Background:Enhancing the quality of images from retinal,MRI and echocardiography imaging shows promise with SR-GANs for medical imaging use.Using these networks,it is possible to produce high-quality images even from low-quality medical scans.Methods:To do this,SR-GANs make use of growth from low to high resolutions in two 2×stages,multiple sizes of filters and powerful loss functions.The medical super-resolution network and denoising SR-GAN focus on problems such as image noise and artifacts to improve a photo’s stability,ability to extract features and how it looks.Results:Assessment by numbers has found that using SR-GAN-based approaches leads to marked improvements such as increases in the PSNR by up to 4.85 dB and improvements in the SSIM by between 0.04 and 0.05.Such improvements are better than traditional super-resolution methods which help doctors achieve clear images of the mitral valve in cardiac ultrasonography.Conclusion:It is anticipated that applying SR-GANs in clinical tasks will increase the accuracy of diagnoses,ease the workload for patients and widen the application of super-resolution methods in various medical procedures.The results prove that SR-GANs improve the picture quality of echocardiograms used for diagnosing mitral valve problems.Having proven the model in research settings,future studies should try to apply it to real-world clinical cases,test for its use across a range of imaging devices and perfect the system to ensure it is efficient for use in medical settings.展开更多
This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the traini...This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the training dataset,and one solution is applied to improve the distribution of the training data by augmenting minority class samples using a deep convolutional generative adversarial network.Experi.mental results demonstrate that retraining the deep learning model with the newly generated dataset leads to a new fast radio burst classifier,which effectively reduces false positives caused by periodic wide-band impulsive radio frequency interference,thereby enhancing the performance of the search pipeline.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 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 ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Gener...The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Generative adversarial networks(GANs)have also garnered increasing research interest recently due to their remarkable ability to generate data.This paper investigates the application of(GANs)in(IDS)and explores their current use within this research field.We delve into the adoption of GANs within signature-based,anomaly-based,and hybrid IDSs,focusing on their objectives,methodologies,and advantages.Overall,GANs have been widely employed,mainly focused on solving the class imbalance issue by generating realistic attack samples.While GANs have shown significant potential in addressing the class imbalance issue,there are still open opportunities and challenges to be addressed.Little attention has been paid to their applicability in distributed and decentralized domains,such as IoT networks.Efficiency and scalability have been mostly overlooked,and thus,future works must aim at addressing these gaps.展开更多
Extensive research has explored human motion generation,but the generated sequences are influenced by different motion styles.For instance,the act of walking with joy and sorrow evokes distinct effects on a character...Extensive research has explored human motion generation,but the generated sequences are influenced by different motion styles.For instance,the act of walking with joy and sorrow evokes distinct effects on a character’s motion.Due to the difficulties in motion capture with styles,the available data for style research are also limited.To address the problems,we propose ASMNet,an action and style-conditioned motion generative network.This network ensures that the generated human motion sequences not only comply with the provided action label but also exhibit distinctive stylistic features.To extract motion features from human motion sequences,we design a spatial temporal extractor.Moreover,we use the adaptive instance normalization layer to inject style into the target motion.Our results are comparable to state-of-the-art approaches and display a substantial advantage in both quantitative and qualitative evaluations.The code is available at https://github.com/ZongYingLi/ASMNet.git.展开更多
Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworth...Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.展开更多
Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide.Accurately predicting landslide displacement enables effective early warning and risk management.However,the limit...Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide.Accurately predicting landslide displacement enables effective early warning and risk management.However,the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models,such as state-of-the-art machine learning(ML)models.To address these challenges,this study proposes a data augmentation framework that uses generative adversarial networks(GANs),a recent advance in generative artificial intelligence(AI),to improve the accuracy of landslide displacement prediction.The framework provides effective data augmentation to enhance limited datasets.A recurrent GAN model,RGAN-LS,is proposed,specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data.A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data.Then,the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory(LSTM)networks and particle swarm optimization-support vector machine(PSO-SVM)models for landslide displacement prediction tasks.Results on two landslides in the Three Gorges Reservoir(TGR)region show a significant improvement in LSTM model prediction performance when trained on augmented data.For instance,in the case of the Baishuihe landslide,the average root mean square error(RMSE)increases by 16.11%,and the mean absolute error(MAE)by 17.59%.More importantly,the model’s responsiveness during mutational stages is enhanced for early warning purposes.However,the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM.Further analysis indicates that an optimal synthetic-to-real data ratio(50%on the illustration cases)maximizes the improvements.This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results.By using the powerful generative AI approach,RGAN-LS can generate high-fidelity synthetic landslide data.This is critical for improving the performance of advanced ML models in predicting landslide displacement,particularly when there are limited training data.Additionally,this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.展开更多
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
基金funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan(Grant No.AP13068032-Development of Methods and Algorithms for Machine Learning for Predicting Pathologies of the Cardiovascular System Based on Echocardiography and Electrocardiography).
文摘The applications of machine learning(ML)in the medical domain are often hindered by the limited availability of high-quality data.To address this challenge,we explore the synthetic generation of echocardiography images(echoCG)using state-of-the-art generative models.We conduct a comprehensive evaluation of three prominent methods:Cycle-consistent generative adversarial network(CycleGAN),Contrastive Unpaired Translation(CUT),and Stable Diffusion 1.5 with Low-Rank Adaptation(LoRA).Our research presents the data generation methodol-ogy,image samples,and evaluation strategy,followed by an extensive user study involving licensed cardiologists and surgeons who assess the perceived quality and medical soundness of the generated images.Our findings indicate that Stable Diffusion outperforms both CycleGAN and CUT in generating images that are nearly indistinguishable from real echoCG images,making it a promising tool for augmenting medical datasets.However,we also identify limitations in the synthetic images generated by CycleGAN and CUT,which are easily distinguishable as non-realistic by medical professionals.This study highlights the potential of diffusion models in medical imaging and their applicability in addressing data scarcity,while also outlining the areas for future improvement.
基金supported by Interdisciplinary Innova-tion Project of“Bioarchaeology Laboratory”of Jilin University,China,and“MedicineþX”Interdisciplinary Innovation Team of Norman Bethune Health Science Center of Jilin University,China(Grant No.:2022JBGS05).
文摘Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.
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 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.
基金funded by the Bavarian State Ministry of Science,Research and Art(Grant number:H.2-F1116.WE/52/2)。
文摘In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.
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The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio map serves as a crucial tool for describing signal strength distribution within the current electromagnetic environment.However,most existing algorithms rely on sparse measurements of radio strength,disregarding the impact of building information.In this paper,we propose a spectrum cartography(SC)algorithm that eliminates the need for relying on sparse ground-based radio strength measurements by utilizing a satellite network to collect data on buildings and transmitters.Our algorithm leverages Pix2Pix Generative Adversarial Network(GAN)to construct accurate radio maps using transmitter information within real geographical environments.Finally,simulation results demonstrate that our algorithm exhibits superior accuracy compared to previously proposed methods.
文摘Background:Enhancing the quality of images from retinal,MRI and echocardiography imaging shows promise with SR-GANs for medical imaging use.Using these networks,it is possible to produce high-quality images even from low-quality medical scans.Methods:To do this,SR-GANs make use of growth from low to high resolutions in two 2×stages,multiple sizes of filters and powerful loss functions.The medical super-resolution network and denoising SR-GAN focus on problems such as image noise and artifacts to improve a photo’s stability,ability to extract features and how it looks.Results:Assessment by numbers has found that using SR-GAN-based approaches leads to marked improvements such as increases in the PSNR by up to 4.85 dB and improvements in the SSIM by between 0.04 and 0.05.Such improvements are better than traditional super-resolution methods which help doctors achieve clear images of the mitral valve in cardiac ultrasonography.Conclusion:It is anticipated that applying SR-GANs in clinical tasks will increase the accuracy of diagnoses,ease the workload for patients and widen the application of super-resolution methods in various medical procedures.The results prove that SR-GANs improve the picture quality of echocardiograms used for diagnosing mitral valve problems.Having proven the model in research settings,future studies should try to apply it to real-world clinical cases,test for its use across a range of imaging devices and perfect the system to ensure it is efficient for use in medical settings.
基金supported by the Chinese Academy of Science"Light of West China"Program(2022-XBQNXZ-015)the National Natural Science Foundation of China(11903071)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China and administered by the Chinese Academy of Sciences。
文摘This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the training dataset,and one solution is applied to improve the distribution of the training data by augmenting minority class samples using a deep convolutional generative adversarial network.Experi.mental results demonstrate that retraining the deep learning model with the newly generated dataset leads to a new fast radio burst classifier,which effectively reduces false positives caused by periodic wide-band impulsive radio frequency interference,thereby enhancing the performance of the search pipeline.
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 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.
文摘The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Generative adversarial networks(GANs)have also garnered increasing research interest recently due to their remarkable ability to generate data.This paper investigates the application of(GANs)in(IDS)and explores their current use within this research field.We delve into the adoption of GANs within signature-based,anomaly-based,and hybrid IDSs,focusing on their objectives,methodologies,and advantages.Overall,GANs have been widely employed,mainly focused on solving the class imbalance issue by generating realistic attack samples.While GANs have shown significant potential in addressing the class imbalance issue,there are still open opportunities and challenges to be addressed.Little attention has been paid to their applicability in distributed and decentralized domains,such as IoT networks.Efficiency and scalability have been mostly overlooked,and thus,future works must aim at addressing these gaps.
基金supported by National Natural Science Foundation of China(No.62203476)Natural Science Foundation of Shenzhen(No.JCYJ20230807120801002).
文摘Extensive research has explored human motion generation,but the generated sequences are influenced by different motion styles.For instance,the act of walking with joy and sorrow evokes distinct effects on a character’s motion.Due to the difficulties in motion capture with styles,the available data for style research are also limited.To address the problems,we propose ASMNet,an action and style-conditioned motion generative network.This network ensures that the generated human motion sequences not only comply with the provided action label but also exhibit distinctive stylistic features.To extract motion features from human motion sequences,we design a spatial temporal extractor.Moreover,we use the adaptive instance normalization layer to inject style into the target motion.Our results are comparable to state-of-the-art approaches and display a substantial advantage in both quantitative and qualitative evaluations.The code is available at https://github.com/ZongYingLi/ASMNet.git.
基金supported by the National Natural Science Foundation of China(Grant Nos.42141019 and 42261144687)the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(Grant No.2019QZKK0102)+4 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB42010404)the National Natural Science Foundation of China(Grant No.42175049)the Guangdong Meteorological Service Science and Technology Research Project(Grant No.GRMC2021M01)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)for computational support and Prof.Shiming XIANG for many useful discussionsNiklas BOERS acknowledges funding from the Volkswagen foundation.
文摘Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.
基金supported by the Natural Science Foundation of Jiangsu Province(Grant No.BK20220421)the State Key Program of the National Natural Science Foundation of China(Grant No.42230702)the National Natural Science Foundation of China(Grant No.82302352).
文摘Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide.Accurately predicting landslide displacement enables effective early warning and risk management.However,the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models,such as state-of-the-art machine learning(ML)models.To address these challenges,this study proposes a data augmentation framework that uses generative adversarial networks(GANs),a recent advance in generative artificial intelligence(AI),to improve the accuracy of landslide displacement prediction.The framework provides effective data augmentation to enhance limited datasets.A recurrent GAN model,RGAN-LS,is proposed,specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data.A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data.Then,the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory(LSTM)networks and particle swarm optimization-support vector machine(PSO-SVM)models for landslide displacement prediction tasks.Results on two landslides in the Three Gorges Reservoir(TGR)region show a significant improvement in LSTM model prediction performance when trained on augmented data.For instance,in the case of the Baishuihe landslide,the average root mean square error(RMSE)increases by 16.11%,and the mean absolute error(MAE)by 17.59%.More importantly,the model’s responsiveness during mutational stages is enhanced for early warning purposes.However,the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM.Further analysis indicates that an optimal synthetic-to-real data ratio(50%on the illustration cases)maximizes the improvements.This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results.By using the powerful generative AI approach,RGAN-LS can generate high-fidelity synthetic landslide data.This is critical for improving the performance of advanced ML models in predicting landslide displacement,particularly when there are limited training data.Additionally,this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.
