Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstru...Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.展开更多
In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only de...In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.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
In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utiliz...In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.展开更多
In this paper,a new machine learning(ML)model combining conditional generative adversarial networks(CGANs)and active learning(AL)is proposed to predict the body-centered cubic(BCC)phase,face-centered cubic(FCC)phase,a...In this paper,a new machine learning(ML)model combining conditional generative adversarial networks(CGANs)and active learning(AL)is proposed to predict the body-centered cubic(BCC)phase,face-centered cubic(FCC)phase,and BCC+FCC phase of high-entropy alloys(HEAs).Considering the lack of data,CGANs are introduced for data augmentation,and AL can achieve high prediction accuracy under a small sample size owing to its special sample selection strategy.Therefore,we propose an ML framework combining CGAN and AL to predict the phase of HEAs.The arithmetic optimization algorithm(AOA)is introduced to improve the artificial neural network(ANN).AOA can overcome the problem of falling into the locally optimal solution for the ANN and reduce the number of training iterations.The AOA-optimized ANN model trained by the AL sample selection strategy achieved high prediction accuracy on the test set.To improve the performance and interpretability of the model,domain knowledge is incorporated into the feature selection.Additionally,considering that the proposed method can alleviate the problem caused by the shortage of experimental data,it can be applied to predictions based on small datasets in other fields.展开更多
The multimode fiber(MMF)has great potential to transmit high-resolution images with less invasive methods in endoscopy due to its large number of spatial modes and small core diameter.However,spatial modes crosstalk w...The multimode fiber(MMF)has great potential to transmit high-resolution images with less invasive methods in endoscopy due to its large number of spatial modes and small core diameter.However,spatial modes crosstalk will inevitably occur in MMFs,which makes the received images become speckles.A conditional generative adversarial network(GAN)composed of a generator and a discriminator was utilized to reconstruct the received speckles.We conduct an MMF imaging experimental system of transmitting over 1 m MMF with a 50μm core.Compared with the conventional method of U-net,this conditional GAN could reconstruct images with fewer training datasets to achieve the same performance and shows higher feature extraction capability.展开更多
基金the support from the National Key R&D Program of China underGrant(Grant No.2020YFA0711700)the National Natural Science Foundation of China(Grant Nos.52122801,11925206,51978609,U22A20254,and U23A20659)G.W.is supported by the National Natural Science Foundation of China(Nos.12002303,12192210 and 12192214).
文摘Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.
基金This work was supported by the Shanxi Province Applied Basic Research Project,China(Grant No.201901D111100).Xiaoli Hao received the grant,and the URL of the sponsors’website is http://kjt.shanxi.gov.cn/.
文摘In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.
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supported in part by the National Science Fund for Distinguished Young Scholars under Grant 61925102in part by the National Natural Science Foundation of China(62201087&92167202&62101069&62201086)in part by the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.
基金supported by the Key Scientific and Technological Project of Henan Province (Grant No.212102210112)the National Natural Science Foundation of China (Grant No.52071298)the Strategic Research and Consulting Project of Chinese Academy of Engineering (Grant No.2022HENYB05)。
文摘In this paper,a new machine learning(ML)model combining conditional generative adversarial networks(CGANs)and active learning(AL)is proposed to predict the body-centered cubic(BCC)phase,face-centered cubic(FCC)phase,and BCC+FCC phase of high-entropy alloys(HEAs).Considering the lack of data,CGANs are introduced for data augmentation,and AL can achieve high prediction accuracy under a small sample size owing to its special sample selection strategy.Therefore,we propose an ML framework combining CGAN and AL to predict the phase of HEAs.The arithmetic optimization algorithm(AOA)is introduced to improve the artificial neural network(ANN).AOA can overcome the problem of falling into the locally optimal solution for the ANN and reduce the number of training iterations.The AOA-optimized ANN model trained by the AL sample selection strategy achieved high prediction accuracy on the test set.To improve the performance and interpretability of the model,domain knowledge is incorporated into the feature selection.Additionally,considering that the proposed method can alleviate the problem caused by the shortage of experimental data,it can be applied to predictions based on small datasets in other fields.
基金supported by the National Key R&D Program of China(No.2018YFB2201803)the National Natural Science Foundation of China(Nos.61821001,61901045,and 61625104)。
文摘The multimode fiber(MMF)has great potential to transmit high-resolution images with less invasive methods in endoscopy due to its large number of spatial modes and small core diameter.However,spatial modes crosstalk will inevitably occur in MMFs,which makes the received images become speckles.A conditional generative adversarial network(GAN)composed of a generator and a discriminator was utilized to reconstruct the received speckles.We conduct an MMF imaging experimental system of transmitting over 1 m MMF with a 50μm core.Compared with the conventional method of U-net,this conditional GAN could reconstruct images with fewer training datasets to achieve the same performance and shows higher feature extraction capability.