Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to existing ensemble and baseline models.The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error(MAPE)of 4.476 and increase prediction accuracy.

Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and Ghosh.This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training process.This inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original distribution.Notably,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text data.The exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original data.The methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation technique.Overall,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using NLPAug.The increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in F1-score.The proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.

基于Regression GAN的原油总氢物性预测方法

针对生成对抗网络(Generative adversarial network,GAN)不适用于原油物性回归预测的问题,本文提出一种回归生成对抗网络(Regression GAN,RGAN)结构,该结构由生成模型G、判别模型D及回归模型R组成.通过判别模型D与生成模型G间的对抗学习,D提取原油物性核磁共振氢谱(~1H NMR)谱图的潜在特征.首层潜在特征是样本空间的浅层表示利于解决回归问题,采用首层潜在特征建立回归模型R,提高了预测的精度及稳定性.通过增加条件变量和生成样本间的互信息约束,并采用回归模型R的MSE损失函数估计互信息下界,生成模型G产生更真实的样本.实验结果表明,RGAN有效地提高了原油总氢物性回归预测精度及稳定性,同时加快了生成模型的收敛速度,提高了谱图的生成质量.

Skip-cycleGAN:一种果园苹果异源图像配准模型

针对有监督的配准模型的性能受限于给定的标签以及循环一致性生成对抗网络训练不稳定,收敛速度较慢,易过拟合,对复杂场景的图像处理效果不佳的问题,基于循环一致性生成对抗网络从3个方面(生成器、鉴别器和损失函数)进行改进,提出一种无监督的异源图像配准模型。生成网络的下采样与上采样之间引入带有特征转换残差层的跳跃连接,可以确保梯度的有效传递,减少前向与反向传播过程中信息损失,实现低级特征和高级特征的结合,从而缓解梯度消失和梯度爆炸,促进神经网络的收敛,有助于网络学习更多的上下文信息。在一个自建果园苹果数据集和两个公共数据集上对模型进行评估,实验得出在改进后的生成器基础上,对于形变比较大的数据集选取70×70 PatchGAN鉴别器更合适,对于形变比较小的数据集选取PixelGAN鉴别器更合适。与8个经典算法进行对比,用6个性能指标进行评估,实验结果表明该模型在异源果园苹果数据集上的综合表现优于对比算法。未来将提升模型对异源图像亮度和对比度的鲁棒性,并进行轻量化模型的工作。

基于条件生成对抗网络的稀疏样本回归预测模型

自生成对抗网络提出以来,基于生成对抗网络的拓展模型在图像处理等领域均有显著成效,但其在回归预测问题上的应用较少.在概率回归过程中,通过构建条件概率分布模拟特征与实际目标之间的潜在关系是一种常用方法,但在实际问题中条件概率分布过于复杂,似然估计难以捕捉.为此,首先分析高斯过程回归在稀疏样本回归预测中的预测精度,将其作为稀疏样本回归预测的基准线.通过分析条件生成对抗网络的构造,提出利用条件生成对抗网络解决稀疏样本回归预测问题.最终通过对比分析4种非线性模拟数据回归预测结果,发现提出的条件生成对抗网络模型相较基于Matern32核函数的高斯过程回归具有更好的预测精度.

采油过程多尺度状态特征生成的有杆泵动态液面预测

动态液面是反映油藏供液能力的重要参数,生产现场多采用回声仪进行检测,其检测效率低,无法连续检测,存在安全隐患。针对现有基于数据驱动的预测方法由于各种原因造成历史数据不足,进而导致建模困难的问题,通过对机理进行分析,找出与动态液面关系密切的多尺度状态特征;采用生成对抗网络生成状态特征,解决历史数据不足的问题;仿真实验表明生成的数据可以用于建立动态液面预测模型,并确定采用支持向量回归作为建模方法;最后在油田生产现场使用本文提出的建模方法,实际预测结果表明本方法的有效性,满足油田工程应用要求。

UPLC-QDA与机器学习区分川贝母商品规格的研究及数据增强技术应用探讨

该文建立基于机器学习技术的准确预测川贝母商品规格的方法,并且对数据增强技术在药物分析领域的应用进行探讨。对30批样品的UPLC-QDA多通道叠加数据使用correlation optimized warping(COW)算法进行峰校正,并对数据进行归一化处理,通过聚类分析、主成分分析、相关性分析等非监督学习方法,掌握数据大致特点,然后使用逻辑回归算法对数据进行监督学习,并使用条件表格生成对抗网络(condition tabular generative adversarial networks,CTGAN)对应生成大量数据,与真实数据分别训练生成逻辑回归分类模型,并对这些模型进行评价。按照真实数据训练的逻辑回归模型交叉验证和测试集验证正确率分别为0.95和1.00,真实数据与CTGAN生成数据一起训练的逻辑回归模型交叉验证和测试集验证正确率分别为0.99和1.00。结果表明,机器学习可以准确地根据UPLC-QDA检测数据预测松贝、青贝和炉贝分类;CTGAN生成数据可以在一定程度上弥补药物分析中数据量不足的问题,提高机器学习模型的精度及预测的能力。

优化GAN的风电机组齿轮箱故障诊断方法

针对风电机组齿轮箱故障诊断准确率低的问题,提出了一种逻辑回归与遗传算法优化生成对抗网络(GAN)的齿轮箱故障诊断方法。该方法采用逻辑回归与遗传算法优化GAN模型,首先,输入信号向量化编码通过轮盘式选择对宏基因等位交叉;然后,用最小二乘变异替换等位编码串重构表征向量,并输入卷积网络进行二次迭代;最后,构建逻辑回归辅助分类器表征决策边界,依据回归曲线实现判别器的分类与诊断。实验结果表明,所提方法的故障诊断准确率达到99.72%,证明该方法实现了样本数据的增强和诊断准确率的提高。

基于生成对抗网络的负荷序列随机场景生成方法

随着可再生能源逐步渗透,电力系统随机性不断加强,其不确定性为调度、规划、运行带来了更大的挑战,因此需要研究针对不确定进行建模的方法。提出一种基于生成对抗网络的负荷场景随机生成方法,该方法基于深度卷积生成对抗网络架构,以JS散度作为目标函数,对生成器以及判别器交替进行训练。针对生成负荷序列质量的衡量,从数据多样性以及锐度2个方面,提出TSTR(train on synthetic test on real)以及TRTS(test on real train on synthetic)2个指标,基于支撑向量回归模型进行判断,实验结果表明,随着训练的进行,生成器产生的数据质量逐渐提高,且当训练完成时可以产生满足多样性以及锐度要求的数据。

Synthesis of True Color Images from the Fengyun Advanced Geostationary Radiation Imager

The production of true color images requires observational data in the red,green,and blue(RGB)bands.The Advanced Geostationary Radiation Imager(AGRI)onboard China’s Fengyun-4(FY-4)series of geostationary satellites only has blue and red bands,and we therefore have to synthesize a green band to produce RGB true color images.We used random forest regression and conditional generative adversarial networks to train the green band model using Himawari-8 Advanced Himawari Imager data.The model was then used to simulate the green channel reflectance of the FY-4 AGRI.A single-scattering radiative transfer model was used to eliminate the contribution of Rayleigh scattering from the atmosphere and a logarithmic enhancement was applied to process the true color image.The conditional generative adversarial network model was better than random forest regression for the green band model in terms of statistical significance(e.g.,a higher determination coefficient,peak signal-to-noise ratio,and structural similarity index).The sharpness of the images was significantly improved after applying a correction for Rayleigh scattering,and the images were able to show natural phenomena more vividly.The AGRI true color images could be used to monitor dust storms,forest fires,typhoons,volcanic eruptions,and other natural events.