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Conditional Generative Adversarial Network-Based Travel Route Recommendation 认领 引用
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作者 Sunbin Shin Luong Vuong Nguyen +3 位作者 Grzegorz J.Nalepa Paulo Novais Xuan Hau Pham Jason J.Jung 《Computers, Materials & Continua》 SCIE EI 2026年第1期1178-1217,共40页
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
关键词 Travel route recommendation conditional generative adversarial network heterogeneous information network anchor-and-expand algorithm
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EGAIN: Enhanced Generative Adversarial Networks for Imputing Missing Values 认领 引用
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作者 Abolfazl Saghafi Soodeh Moallemian +1 位作者 Miray Budak Rutvik Deshpande 《Computers, Materials & Continua》 SCIE EI 2026年第8期2241-2255,共15页
Missing data remain a persistent challenge in statistical analysis and machine learning because many predictive methods require complete observations.Generative Adversarial Imputation Networks(GAIN)offer a flexible de... Missing data remain a persistent challenge in statistical analysis and machine learning because many predictive methods require complete observations.Generative Adversarial Imputation Networks(GAIN)offer a flexible deep-learning approach for missing value imputation,but their practical use is limited by convergence instability,sensitivity to hyperparameter selection,and dependence on outdated software implementations.To address these limitations,we propose Enhanced Generative Adversarial Imputation Networks(EGAIN),a modernized extension of GAIN implemented in TensorFlow 2.x.EGAIN incorporates convolution-based generator and discriminator networks,a channel-stacked representation of the data and mask,and checkpoint-based training diagnostics to improve stability and usability.EGAIN was evaluated on five benchmark datasets under multiple Missing Completely At Random(MCAR)settings and compared with the original GAIN implementation and median imputation.Across most evaluated conditions,EGAIN achieved lower root mean squared error(RMSE)and showed greater robustness,particularly when missingness was concentrated in a subset of variables.These results indicate that EGAIN provides a more stable and reproducible framework for missing data imputation in tabular datasets. 展开更多
关键词 Missing value imputation generative adversarial network tabular data imputation missing completely at random convolutional architectures training stability
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Predicting permeability coefficients of earth-rock material using an improved generative adversarial network and explainable ensemble learning under small sample conditions 认领 引用
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作者 Chengyu YU Hongling YU +4 位作者 Xiaofeng QU Baoxi LIU Liangsi XU Xinyu LIU Xiangyu CHEN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第3期215-230,共16页
Accurate prediction of the permeability coefficient is crucial for evaluating the compaction quality of earthworks.However,during the compaction process,on-site testing is often time-consuming and expensive,leading to... Accurate prediction of the permeability coefficient is crucial for evaluating the compaction quality of earthworks.However,during the compaction process,on-site testing is often time-consuming and expensive,leading to fewer samples,which affects prediction accuracy.Moreover,most current predictive models have limited capabilities and tend to be black-box models with poor explainability.To overcome these issues,in this study,we proposed a new method to predict the permeability coefficient of earth-rock material based on an improved generative adversarial network(GAN)and explainable osprey optimization algorithm–Huber loss–light gradient boosting machine(OOA–HL–LightGBM).Firstly,by introducing the Wasserstein distance as the loss function into the conditional generative adversarial network(CGAN),the Wasserstein conditional generative adversarial network(WCGAN)was proposed to generate high-quality data,addressing the issue of insufficient information caused by small samples.Furthermore,by incorporating material and compaction parameters as inputs,a high-accuracy permeability coefficient prediction model was developed using LightGBM with the Huber loss function and the OOA.Finally,the Shapley additive explanation(SHAP)method was introduced into OOA–HL–LightGBM to analyze the specific roles of different features within the dataset to enhance the credibility of the prediction results.The proposed method was applied to a large-scale high-core rockfill dam in southwestern China to thoroughly verify its effectiveness and superiority. 展开更多
关键词 Permeability coefficient prediction Light gradient boosting machine(LightGBM) Wasserstein conditional generative adversarial network(WCGAN) Shapley additive explanation(SHAP)
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Conveyor-Belt Detection of Conditional Deep Convolutional Generative Adversarial Network 认领 引用 被引量：2
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作者 Xiaoli Hao Xiaojuan Meng +2 位作者 Yueqin Zhang JinDong Xue Jinyue Xia 《Computers, Materials & Continua》 SCIE EI 2021年第11期2671-2685,共15页
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
关键词 Multi-class detection conditional deep convolution generative adversarial network conveyor belt tear skip-layer connection
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Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites 认领 引用 被引量：1
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作者 Chengkan Xu Xiaofei Wang +2 位作者 Yixuan Li Guannan Wang He Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期957-974,共18页
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
关键词 Periodic composites localized stress recovery conditional generative adversarial network
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Data-Driven Structural Topology Optimization Method Using Conditional Wasserstein Generative Adversarial Networks with Gradient Penalty 认领 引用
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作者 Qingrong Zeng Xiaochen Liu +2 位作者 Xuefeng Zhu Xiangkui Zhang Ping Hu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第12期2065-2085,共21页
Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challe... Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples. 展开更多
关键词 Real-time topology optimization conditional generative adversarial networks dimension curse CMES,2024,vol.141,no.3
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Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks 认领 引用 被引量：39
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作者 Tuan-Feng Zhang Peter Tilke +3 位作者 Emilien Dupont Ling-Chen Zhu Lin Liang William Bailey 《Petroleum Science》 SCIE CAS CSCD 2019年第3期541-549,共9页
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
关键词 Geological facies Geomodeling Data conditioning Generative adversarial networks
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An improved conditional denoising diffusion GAN for Mach number field reconstruction in a multi-tunnel combined inlet based on sparse parameter information 认领 引用
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作者 Ke MIN Fan LEI +2 位作者 Jiale ZHANG Chengxiang ZHU Yancheng YOU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期169-190,共22页
The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To... The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields. 展开更多
关键词 Flow field reconstruction Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN) Mode transition Sparse parameter information Three-dimensional inward-tunning combined inlet
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A Missing Data Complement Method Based on 3D Convolutional Neural Network and CGAN for a Distribution Network 认领 引用
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作者 Kewen Li Xiaoyong Yu +1 位作者 Shifeng Ou Jueming Pan 《Energy Engineering》 EI 2026年第9期132-156,共25页
The increasing integration of renewable energy sources(e.g.,wind and solar power)into distribution grids and the development of new,source-grid-load-storage coordinated power systems have led to a substantial expansio... The increasing integration of renewable energy sources(e.g.,wind and solar power)into distribution grids and the development of new,source-grid-load-storage coordinated power systems have led to a substantial expansion in the volume of situational awareness data in the distribution networks.Moreover,the transmission of low-voltage distribution measurement data via a power line carrier(PLC)is often susceptible to packet loss and,consequently,data gaps.To address these issues,this paper proposes a data completion method using a conditional generative adversarial network(CGAN)integrated with a three-dimensional convolutional neural network(3D-CNN).This approach leverages the ability of CNNs to extract and fuse multidimensional spatiotemporal features and the power of GANs(generative adversarial networks)for data augmentation.Firstly,a 3D-CNN is trained to establish a mapping between the spatiotemporal context of the measured data and the target missing data.Secondly,a CGAN is practicing via adversarial training to establish a data completion model for the distribution networks.Finally,the simulations of the IEEE 14-bus and 33-bus systems demonstrate the proposed approach's performance improvement in the distribution networks compared with that of conventional methods in terms of root mean square error,spatiotemporal correlation,and maximum volatility amplitude,which are the typical measuring metrics. 展开更多
关键词 Distribution network data estimation 3D convolutional neural network conditional generative adversarial networks spatiotemporal correlation
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An inverse design method for supercritical airfoil based on conditional generative models 认领 引用 被引量：21
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作者 Jing WANG Runze LI +4 位作者 Cheng HE Haixin CHEN Ran CHENG Chen ZHAI Miao ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第3期62-74,共13页
Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learnin... Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning.A Conditional Variational Auto Encoder(CVAE)and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks(WGAN),are conducted as generative models.They are used to generate target wall Mach distributions for the inverse design that matches specified features,such as locations of suction peak,shock and aft loading.Qualitative and quantitative results show that both adopted generative models can generate diverse and realistic wall Mach number distributions satisfying the given features.The CVAE-GAN model outperforms the CVAE model and achieves better reconstruction accuracies for all the samples in the dataset.Furthermore,a deep neural network for nonlinear mapping is adopted to obtain the airfoil shape corresponding to the target wall Mach number distribution.The performances of the designed deep neural network are fully demonstrated and a smoothness measurement is proposed to quantify small oscillations in the airfoil surface,proving the authenticity and accuracy of the generated airfoil shapes. 展开更多
关键词 Conditional Variational AutoEncoder(CVAE) Deep learning Generative Adversarial Networks(GAN) Generative models Inverse design Supercritical airfoil
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基于CTGAN的自动驾驶车辆交通事故关键诱因识别 认领 引用
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作者 张志清 于晓正 +2 位作者 朱雷鹏 孙玉凤 李祎昕 《华南理工大学学报（自然科学版）》 EI CAS CSCD 北大核心 2025年第10期14-28,共15页
明晰自动驾驶车辆交通事故机理是有效防控安全风险的重要前提。自动驾驶车辆交通事故诱因分析通常基于小样本和不平衡数据进行建模,但这类模型对于少数类预测精度低。基于数据增强的分析框架可以提高模型对于少数类的预测精度。通过条... 明晰自动驾驶车辆交通事故机理是有效防控安全风险的重要前提。自动驾驶车辆交通事故诱因分析通常基于小样本和不平衡数据进行建模,但这类模型对于少数类预测精度低。基于数据增强的分析框架可以提高模型对于少数类的预测精度。通过条件表格生成对抗网络(CTGAN)、联合生成对抗网络(CopulaGAN)以及合成少数过采样(SMOTE)、自适应过采样(ADASYN)技术增加样本量,平衡数据集,对比不同方法的合成数据质量;基于合成数据,对逻辑回归(LR)、决策树(DT)、随机森林(RF)、极端梯度提升(XGB)、支持向量机(SVM)5种分类算法进行评估,采用召回率、特异性、加权F_1分数及曲线下面积(AUC)等指标确定最优组合;最后结合沙普利可加解释(SHAP)框架量化事故关键诱因重要度。结果表明:CTGAN生成数据的边际分布得分(0.96)和相关性得分(0.92)最高,合成数据的平均质量为0.94,显著优于其他方法;CTGAN与随机森林算法结合时,模型在召回率(0.82)、特异性(0.84)、AUC(0.86)等指标上均表现优异,在包含10%标签噪声的测试集中仍保持鲁棒性(召回率提升至0.88),进一步验证了其在复杂场景中的适用性。关键诱因分析表明,路面状况(潮湿状态显著增加受伤风险)、夜间行车(低光照导致传感器性能下降)、交叉口及街道化程度(复杂场景增加检测延迟)是导致事故的核心因素。该研究为自动驾驶测试场景搭建及道路基础设施改造提供了关键依据。 展开更多
关键词 自动驾驶车辆 小样本量 数据不平衡 条件表格生成对抗网络 事故预测
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基于CTGAN和逻辑回归的企业员工流失预测及影响因素研究 认领 引用
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作者 金艺鸥 王宁若 +1 位作者 唐昊 王淼 《云南民族大学学报（自然科学版）》 CAS 2025年第5期597-610,共14页
通过分析影响员工离职的关键因素,构建条件表格对抗生成网络-逻辑回归(CTGANLR)预测模型.首先,选用了某公司的开源人力资源数据集并进行了预处理.由于离职样本数量较少,采用条件表格对抗生成网络(CTGAN)进行过采样,以解决数据不平衡的问... 通过分析影响员工离职的关键因素,构建条件表格对抗生成网络-逻辑回归(CTGANLR)预测模型.首先,选用了某公司的开源人力资源数据集并进行了预处理.由于离职样本数量较少,采用条件表格对抗生成网络(CTGAN)进行过采样,以解决数据不平衡的问题.其次,在原始数据集和平衡后的数据集上,利用逻辑回归、决策树、随机森林和梯度提升树等多种机器学习算法进行员工流失预测,结果表明CTGAN-LR在各项指标上表现最佳.最后,研究探讨了影响员工离职的主要因素,通过特征重要性分析和因果推断确认了这些因素的显著性.同时,通过生存分析为企业提供了动态视角,以帮助制定更有效的人力资源管理策略.研究结果为企业制定针对性的留人策略提供了实证依据,并强调了提升员工满意度和优化薪酬结构的重要性. 展开更多
关键词 员工流失 不平衡数据集 条件表格对抗生成网络 逻辑回归
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基于小样本数据集的煤层顶板突水溃砂危险性预测 认领 引用 被引量：1
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作者 张文泉 李子旭 +2 位作者 朱先祥 邱伟 张承杰 《煤田地质与勘探》 EI CAS CSCD 北大核心 2026年第3期126-138,共13页
【目的】我国华东、华北地区松散层厚度大、基岩薄,突水溃砂事故频发,实现顶板突水溃砂危险性精准预测对保障煤矿安全生产意义重大。但突水溃砂致灾机理极为复杂,涉及多因素耦合作用。现场实测面临高风险、高成本等问题,导致数据获取困... 【目的】我国华东、华北地区松散层厚度大、基岩薄,突水溃砂事故频发,实现顶板突水溃砂危险性精准预测对保障煤矿安全生产意义重大。但突水溃砂致灾机理极为复杂,涉及多因素耦合作用。现场实测面临高风险、高成本等问题,导致数据获取困难,样本量严重不足,制约了传统预测模型的精度与性能,探索适用于小样本场景的有效预测方法迫在眉睫。【方法】梳理分析近松散层工作面现场实测数据与历史案例,确定底部含水层厚度、基岩厚度等11个影响因素,构建原始样本数据集。运用斯皮尔曼相关性分析揭示各因素的内在联系及相关性;基于条件表格生成对抗网络(CTGAN)、探测粒子群优化算法(DPSO)、随机森林算法(RF)构建突水溃砂危险性预测模型(CTGAN−DPSO−RF),探讨CTGAN合成数据的质量,并与DPSO−SVM、DPSO−XGBoost模型进行对比,最后结合工程实例验证模型有效性。【结果和结论】11个突水溃砂影响因素中,垮落带高度与采高相关性最大,相关系数为0.93;松散层底部含水层水压与导水裂隙带发育高度相关性最小。CTGAN合成数据与原始数据高度相似,综合质量分数达85.03%;DPSO寻优后最优适应度为0.9265,优于PSO算法;CTGAN−DPSO−RF模型测试集AC、PW、RW、F1W均达到1,全面优于对比模型,工作面预测结果与实际开采情况一致,该模型通过合成高质量数据扩充样本集、优化超参数,有效解决小样本下传统模型精度低、性能差的问题,为厚松散层薄基岩条件下煤层顶板突水溃砂危险性预测提供了新方法。 展开更多
关键词 煤层顶板 厚松散层薄基岩 突水溃砂 小样本数据 条件表格生成对抗网络 探测粒子群优化算法 危险性预测
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基于条件生成对抗网络的柔性薄壁结构装配偏差预测方法 认领 引用 被引量：2
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作者 潘炜 赵勇 +3 位作者 刘禹铭 林清源 葛恩德 汪玮 《上海交通大学学报》 EI CAS CSCD 北大核心 2026年第5期786-799,共14页
大型薄壁结构装配时会因零件制造误差和装配变形的耦合影响产生整体柔性变形,现有的偏差分析方法在处理薄壁结构的柔性偏差时难以兼顾各偏差的耦合作用.对此,提出一种基于条件生成对抗网络(cGAN)的偏差预测方法,分析多源偏差特点,提出... 大型薄壁结构装配时会因零件制造误差和装配变形的耦合影响产生整体柔性变形,现有的偏差分析方法在处理薄壁结构的柔性偏差时难以兼顾各偏差的耦合作用.对此,提出一种基于条件生成对抗网络(cGAN)的偏差预测方法,分析多源偏差特点,提出对各偏差因素的图像融合策略,构建基于cGAN架构的图到图转换模型以预测薄壁结构的柔性偏差;以曲面蒙皮对接为研究对象,训练和测试偏差预测模型,搭建模拟装配实验台进行实物实验.实验结果表明:基于cGAN网络的偏差预测模型能以小规模数据集实现对柔性薄壁结构装配偏差的预测,相比传统方法在精度和效率上都具有优势,是一种具有潜力的偏差分析新方法. 展开更多
关键词 大型薄壁结构 偏差表征 偏差分析 条件生成对抗网络
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信用风险不平衡数据的表格生成对抗网络优化与分类 认领 引用 被引量：1
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作者 王轶群 王笑 高燕程 《计算机科学与探索》 CSCD 北大核心 2026年第2期561-573,共13页
人工智能在信用风险评估中能有效识别风险并提升决策效率,然而,现有信用风险数据普遍存在类别不平衡问题,导致模型在预测时偏向多数类,影响评估的准确性和可靠性。针对数据不平衡问题,提出一种融合变分自编码器(VAE)和条件表格生成对抗... 人工智能在信用风险评估中能有效识别风险并提升决策效率,然而,现有信用风险数据普遍存在类别不平衡问题,导致模型在预测时偏向多数类,影响评估的准确性和可靠性。针对数据不平衡问题,提出一种融合变分自编码器(VAE)和条件表格生成对抗网络(CTGAN)的混合生成模型(VCTGAN),用于合成高质量平衡数据集。通过VAE中的隐变量学习真实数据的关键特征和潜在分布,生成结构化隐变量作为原始CTGAN的输入;在数据生成器中引入自注意力机制用于更好地捕捉不平衡数据的突出特征;在判别器中加入对比损失模块来增强生成数据的类别间差异,达到提高生成数据质量的目的。通过在Taiwan Credit和Give Me Some Credit两个基准数据集上的系统实验验证,分别取得了89.91%和96.89%的最佳分类准确率,结果表明这种改进方法在处理信用数据不平衡方面明显优于传统方法。消融实验进一步验证了各组件对性能的贡献,证实了所提方法的合理性和有效性。它不仅生成高质量的平衡数据集,而且提高模型识别少数类别的能力,为解决金融领域的数据不平衡问题提供了新的技术方案。 展开更多
关键词 条件表格生成对抗网络(CTGAN) 生成模型 不平衡数据集 机器学习 信用风险评估
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兼顾常规与极端场景的城市配电网灵活性资源规划 认领 引用 被引量：1
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作者 程嵩晴 聂彤 +2 位作者 卢国强 滕云 陈哲 《电力系统自动化》 EI CSCD 北大核心 2026年第14期153-165,共13页
新能源的强不确定性叠加极端天气频发给新型电力系统建设带来了巨大挑战;同时,电动汽车负荷逐渐增加也对配电网运行提出了新的要求。因此,文中提出了一种兼顾常规与极端场景的配电网灵活性资源规划方法。首先,分析电动汽车充电站、氢储... 新能源的强不确定性叠加极端天气频发给新型电力系统建设带来了巨大挑战;同时,电动汽车负荷逐渐增加也对配电网运行提出了新的要求。因此,文中提出了一种兼顾常规与极端场景的配电网灵活性资源规划方法。首先,分析电动汽车充电站、氢储一体站、固定储能电站与移动储能站的灵活性资源运行特性,同时考虑配电网与交通网对灵活性资源规划的要求,建立灵活性资源云-边协同规划模型。然后,建立基于条件生成对抗网络(CGAN)-深度Q网络(DQN)的灵活性资源规划求解网络,采用CGAN与自监督条件生成对抗网络(SS-CGAN)实现常规与极端场景生成,采用DQN实现灵活性资源规划方案求解与优化。最后,以IEEE 33节点配电网与24节点交通网络耦合系统作为算例对所提出的方法进行验证。结果表明,所提方法能够有效生成覆盖真实数据的供需场景,实现配电网内部灵活性资源的规划。 展开更多
关键词 配电网 交通网 极端天气 场景生成 灵活性资源 规划 条件生成对抗网络 深度Q网络
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物理约束型生成对抗网络人工地震动合成方法 认领 引用 被引量：1
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作者 陈苏 崔澳辉 +3 位作者 丁毅 傅磊 王苏阳 李小军 《地震研究》 CSCD 北大核心 2026年第1期111-119,共9页
针对重大工程结构抗震分析中地震动记录稀缺,以及传统合成方法在物理真实性和多分量适应性上的瓶颈问题,基于日本KiK-net台站近11万条地震动记录,提出了一种物理经验引导型生成对抗网络算子(GM-WGANO)人工地震动合成方法。该方法利用生... 针对重大工程结构抗震分析中地震动记录稀缺,以及传统合成方法在物理真实性和多分量适应性上的瓶颈问题,基于日本KiK-net台站近11万条地震动记录,提出了一种物理经验引导型生成对抗网络算子(GM-WGANO)人工地震动合成方法。该方法利用生成对抗网络(GANs)框架,引入傅立叶神经算子(FNO)优化网络结构,结合震级、最小断层距、等效剪切波速、滑动机制和断层构造类别5个物理条件变量,从强震动观测数据中学习地震动的时空特征概率分布,并通过对抗训练生成与真实记录统计特性高度一致的三分量人工时程。结果表明:生成时程在时域上具有与真实记录相近的强震动持时、相位分布及峰值加速度特性;傅立叶谱与观测数据的误差均小于±1倍标准差;地震动峰值加速度(PGA)的对数分布均值与观测数据吻合。 展开更多
关键词 人工地震动合成 生成对抗网络 傅立叶神经算子 多物理条件约束
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基于条件边界均衡生成对抗网络的入室盗窃犯罪预测研究 认领 引用
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作者 张耀峰 姚金伶 +2 位作者 朱艳敏 王睿 张志刚 《计算机工程与应用》 EI CSCD 北大核心 2026年第11期352-361,共10页
犯罪预测是犯罪防控的有效手段之一,精准的犯罪预测对打击犯罪、降低犯罪发生率具有重要意义。针对当前犯罪数据稀疏性等问题,为提升犯罪预测效果,提出基于条件边界均衡生成对抗网络(C-BEGAN)的犯罪预测模型。该模型使用历史犯罪数据作... 犯罪预测是犯罪防控的有效手段之一,精准的犯罪预测对打击犯罪、降低犯罪发生率具有重要意义。针对当前犯罪数据稀疏性等问题,为提升犯罪预测效果,提出基于条件边界均衡生成对抗网络(C-BEGAN)的犯罪预测模型。该模型使用历史犯罪数据作为条件,通过生成模型生成预测的犯罪案发情况,通过判别模型鉴别真实案发情况与生成案发情况,生成模型与判别模型进行交替对抗训练,以达到生成模型生成的案发样本无法被判别模型判别真假的最优结果。以W市入室盗窃接警数据进行实证研究,数值实验表明,与自激点模型和长短期记忆网络模型相比,基于C-BEGAN的犯罪预测模型在犯罪预测效果指数上分别提高了175.92%和10.02%。 展开更多
关键词 犯罪预测 入室盗窃 条件生成对抗网络 边界均衡
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物理信息约束的条件生成对抗网络渗透系数反演研究 认领 引用
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作者 南天 曹文庚 +4 位作者 孙晓悦 李泽岩 任宇 卢瑶 余鸣潇 《水文地质工程地质》 CAS CSCD 北大核心 2026年第4期110-122,共13页
具有物理信息约束的神经网络(physics-informed neural network,PINN)模型已广泛用于地下水水位和水量等问题的正向求解。而对于水文地质参数反演问题,受小样本量和“异参同效现象”影响,单独使用PINN模型往往存在较大的不确定性。为解... 具有物理信息约束的神经网络(physics-informed neural network,PINN)模型已广泛用于地下水水位和水量等问题的正向求解。而对于水文地质参数反演问题,受小样本量和“异参同效现象”影响,单独使用PINN模型往往存在较大的不确定性。为解决上述问题,并提高水文地质参数反演的可解释性,文章将PINN和条件生成对抗网络(conditional generative adversarial network,CGAN)进行耦合,形成了PICGAN(physics-informed conditional generative adversarial network)模型,并设置二维非均质非稳态算例模拟验证模型的适用性。主要结果如下:在5%采样率的算例模拟中,PICGAN模型模拟的水头均方根误差可稳定在0.95 m左右,准确率达89%;渗透系数场均方根误差可稳定在0.69 m/d左右,准确率可达95%,且分布形式与参考场高度一致;而随着采样率的提升,模型全局误差会进一步减小,在采样率达到10%以上后,全局渗透系数反演误差降低至0.35 m/d,准确率达97%。研究表明,PICGAN模型能够高效地用于小样本条件下水文地质参数和流场模拟预测。文章提出的方法可为地下水双向求解问题,尤其是非均质水文地质参数场反演提供新的思路和方法借鉴。 展开更多
关键词 物理信息约束的神经网络 条件生成对抗网络 非均质 承压含水层 参数反演
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基于生成对抗网络和坐标注意力机制的文本生成图像算法 认领 引用
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作者 李云红 张琪琪 +3 位作者 陈锦妮 陈伟重 苏雪平 梁成名 《浙江大学学报（工学版）》 EI CAS CSCD 北大核心 2026年第6期1213-1220,共8页
针对对抗网络生成的图像存在多样性差、总体质量不高的问题,提出基于坐标注意力机制和生成对抗网络的文本生成图像算法(CAT-GAN).采用条件增强计算文本特征向量的均值和协方差矩阵,生成条件变量代替原高维文本特征,解决稀疏性问题.将坐... 针对对抗网络生成的图像存在多样性差、总体质量不高的问题,提出基于坐标注意力机制和生成对抗网络的文本生成图像算法(CAT-GAN).采用条件增强计算文本特征向量的均值和协方差矩阵,生成条件变量代替原高维文本特征,解决稀疏性问题.将坐标注意力机制引入生成器网络的残差块中,构成结合坐标注意力机制的深度融合模块(CA-Block),在捕捉通道间特征长期依赖关系的同时,保留特征的精确位置,增强感兴趣对象的表示.在鉴别器网络中引入空间重构单元,构成特征空间重构模块(SRU-Block).通过权重分离冗余特征并重构,增强鉴别器对特征的表征能力.通过CUB-200、Oxford-102 Flowers及COCO数据集,测试并验证模型.实验结果表明,与StackGAN++、AttnGAN、DAE-GAN、DM-GAN、DT-GAN及DF-GAN等模型相比,所提模型(CAT-GAN)的IS和FID指标值均为最优,IS指标值分别达到5.13、4.10、31.81,FID指标值分别达到14.34、16.76、26.36.所提模型具有更好的可视化效果,证明了所提方法的有效性. 展开更多
关键词 文本生成图像 生成对抗网络(GAN) 条件增强 坐标注意力机制 仿射变换
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