SNP site-drug association prediction algorithm based on denoising variational auto-encoder

Single nucletide polymorphism(SNP)is an important factor for the study of genetic variation in human families and animal and plant strains.Therefore,it is widely used in the study of population genetics and disease related gene.In pharmacogenomics research,identifying the association between SNP site and drug is the key to clinical precision medication,therefore,a predictive model of SNP site and drug association based on denoising variational auto-encoder(DVAE-SVM)is proposed.Firstly,k-mer algorithm is used to construct the initial SNP site feature vector,meanwhile,MACCS molecular fingerprint is introduced to generate the feature vector of the drug module.Then,we use the DVAE to extract the effective features of the initial feature vector of the SNP site.Finally,the effective feature vector of the SNP site and the feature vector of the drug module are fused input to the support vector machines(SVM)to predict the relationship of SNP site and drug module.The results of five-fold cross-validation experiments indicate that the proposed algorithm performs better than random forest(RF)and logistic regression(LR)classification.Further experiments show that compared with the feature extraction algorithms of principal component analysis(PCA),denoising auto-encoder(DAE)and variational auto-encode(VAE),the proposed algorithm has better prediction results.

VAE-ATTGRU模型的股指期货价格预测研究

针对股指期货市场高波动、非平稳、非线性和高信噪比等特性造成的预测难度大的问题,利用变分自编码器(VAE)和循环神经网络(RNN)提出一种基于VAE-ATTGRU的混合深度学习股指期货价格预测模型。利用变分自编码器对股指期货技术指标进行学习,将VAE学习到的潜在因子与原始数据融合实现数据增强,得到更丰富的因子表示;使用循环神经网络对股指期货价格进行预测,发现结合了注意力机制的门控循环单元(ATTGRU)可以对VAE增强后的股指期货数据进行充分学习,对关键特征信息进行捕捉并重新赋予权重。在沪深300股指期货、中证500股指期货和上证50股指期货数据上进行实验,通过均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)和决定系数R2对VAE-ATTGRU模型进行评估,发现其在预测精度上优于其他模型。

结合SE-VAE与M1DCNN的小样本数据下轴承故障诊断

针对轴承故障诊断中故障样本数量少导致诊断正确率低的问题,提出了一种基于注意力机制变分自编码器(SE-VAE)和多尺度一维卷积神经网络(M1DCNN)的轴承故障诊断方法。将轴承数据集的训练集输入到SE-VAE中进行训练,生成与训练样本分布相似的生成样本,并添加到训练集中增加训练集的样本数量。将扩充后的训练集输入到M1DCNN中进行训练,随后将训练好的模型应用于测试集,输出故障诊断结果。实验结果表明,所提方法能够在不同负载的小样本轴承故障数据集上取得较好的故障诊断准确率。

基于改进VAE的传感器异常数据检测方法研究

气体传感器在采样过程中受复杂工业环境影响常常产生异常时间序列数据。传统的时间序列异常检测采用模型预测方法,但没有考虑到时间序列数据的不平衡问题。因此,提出一种基于改进VAE模型的检测方法。首先,将大量正常时序数据与较少且难以标记的异常时序数据进行合并构建成一个不平衡数据集。其次,在传统VAE模型的基础上采用无监督学习方式,在异常检测分类环节引入动态阈值方法增强网络模型的自适应异常检测能力。最后,提出一种时序异常检测的组合损失函数,通过集成交叉熵损失函数和KL散度进一步提升网络参数优化性能。实验结果表明,该方法在精确率、召回率以及F1值等异常检测性能指标上,比原有的方法有所提升。该方法在传感器异常数据检测中有着较好的应用。

基于DTCWT-VAE的弹道中段目标RCS识别

针对弹道目标雷达信号易受环境影响、目标识别准确率低的问题,提出了一种基于双树复小波变换(dual-tree complex wavelet transform,DTCWT)和变分自编码器(variational autoencoder,VAE)的弹道目标雷达散射截面(radar cross section,RCS)识别法。首先,采用DTCWT对弹道目标RCS动态数据进行预处理,再利用VAE提取目标的隐变量特征,最后用支持向量机(support vector machine,SVM)分类器进行识别。实验结果表明,与已有方法相比,该方法具有更高的识别概率,且鲁棒性较好。

基于VAE-GAN和FLCNN的不均衡样本轴承故障诊断方法

针对滚动轴承故障诊断中样本分布不均衡引起的模型泛化能力差、诊断精度低的问题,从两个方面展开研究:(1)故障样本增广,提出结合变分自编码器(VAE)和生成对抗网络(GAN)的VAE-GAN样本增广模型;(2)改进分类算法,提出基于焦点损失(FL)和卷积神经网络(CNN)的FLCNN(focal loss and convolutional neural network)样本分类模型。在此基础上,将VAE-GAN和FLCNN融合,构建VAE-GAN+FLCNN轴承故障诊断模型。首先,将样本量少的故障类输入VAE-GAN模型,通过交替训练编码网络、生成网络和判别网络,学习出真实故障样本的数据分布,从而实现故障样本的增广;然后用增广后的数据样本训练FLCNN分类模型,完成轴承故障识别。试验对比结果表明,所提方法能够有效提升样本不均衡条件下的轴承故障诊断效果,拥有更高的Recall值和F1-score值。

Exploring Variational Auto-encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI

Generative AI models for music and the arts in general are increasingly complex and hard to understand.The field of ex-plainable AI(XAI)seeks to make complex and opaque AI models such as neural networks more understandable to people.One ap-proach to making generative AI models more understandable is to impose a small number of semantically meaningful attributes on gen-erative AI models.This paper contributes a systematic examination of the impact that different combinations of variational auto-en-coder models(measureVAE and adversarialVAE),configurations of latent space in the AI model(from 4 to 256 latent dimensions),and training datasets(Irish folk,Turkish folk,classical,and pop)have on music generation performance when 2 or 4 meaningful musical at-tributes are imposed on the generative model.To date,there have been no systematic comparisons of such models at this level of com-binatorial detail.Our findings show that measureVAE has better reconstruction performance than adversarialVAE which has better musical attribute independence.Results demonstrate that measureVAE was able to generate music across music genres with inter-pretable musical dimensions of control,and performs best with low complexity music such as pop and rock.We recommend that a 32 or 64 latent dimensional space is optimal for 4 regularised dimensions when using measureVAE to generate music across genres.Our res-ults are the first detailed comparisons of configurations of state-of-the-art generative AI models for music and can be used to help select and configure AI models,musical features,and datasets for more understandable generation of music.

Feature-aided pose estimation approach based on variational auto-encoder structure for spacecrafts

Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressive achievements for spacecraft pose estimation.To improve the robustness and interpretability of CNNs,this paper proposes a Pose Estimation approach based on Variational Auto-Encoder structure(PE-VAE)and a Feature-Aided pose estimation approach based on Variational Auto-Encoder structure(FA-VAE),which aim to accurately estimate the 6 DoF pose of a target spacecraft.Both methods treat the pose vector as latent variables,employing an encoder-decoder network with a Variational Auto-Encoder(VAE)structure.To enhance the precision of pose estimation,PE-VAE uses the VAE structure to introduce reconstruction mechanism with the whole image.Furthermore,FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired shape.Comparative evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed,showcasing the significant contribution of VAE structures to accuracy enhancement,and the additional benefit of incorporating global shape prior features.

A novel deep learning framework with variational auto-encoder for indoor air quality prediction

Exposure to poor indoor air conditions poses significant risks to human health, increasing morbidity and mortality rates. Soft measurement modeling is suitable for stable and accurate monitoring of air pollutants and improving air quality. Based on partial least squares (PLS), we propose an indoor air quality prediction model that utilizes variational auto-encoder regression (VAER) algorithm. To reduce the negative effects of noise, latent variables in the original data are extracted by PLS in the first step. Then, the extracted variables are used as inputs to VAER, which improve the accuracy and robustness of the model. Through comparative analysis with traditional methods, we demonstrate the superior performance of our PLS-VAER model, which exhibits improved prediction performance and stability. The root mean square error (RMSE) of PLS-VAER is reduced by 14.71%, 26.47%, and 12.50% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. Additionally, the coefficient of determination (R2) of PLS-VAER improves by 13.70%, 30.09%, and 11.25% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. This research offers an innovative and environmentally-friendly approach to monitor and improve indoor air quality.

基于VAE-CGAN的牦牛等级评定算法

在牦牛高效养殖过程中,牦牛等级评定是牦牛育种工作中的重要环节.为了在牦牛等级评定研究中,降低数据集分布不平衡对牦牛等级预测结果的影响,提出一种基于改进条件生成对抗网络模型的牦牛等级评定模型VAE-CGAN.首先,为获取高质量生成样本,模型通过引入变分自编码器取代条件生成对抗网络输入中的随机噪声,降低了随机变量带来的不确定性.此外,模型将牦牛标签作为条件信息输入到生成对抗模型中来获取指定类别的生成样本,生成样本及训练样本则会被用于训练深度神经网络分类器.实验结果显示,模型整体预测准确率达到了97.9%.而且与生成对抗网络相比较,在数量较少的特级牦牛等级预测上的精准率、召回率和F1值分别提升了16.7%、16.6%和19.4%.实验结果表明该模型可以实现高精准度和低误分类率的牦牛等级分类.

Predicting the Antigenic Variant of Human Influenza A(H3N2) Virus with a Stacked Auto-Encoder Model

The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic variants in time. Here, we built a stacked auto-encoder (SAE) model for predicting the antigenic variant of human influenza A(H3N2) viruses based on the hemagglutinin (HA) protein sequences. The model achieved an accuracy of 0.95 in five-fold cross-validations, better than the logistic regression model did. Further analysis of the model shows that most of the active nodes in the hidden layer reflected the combined contribution of multiple residues to antigenic variation. Besides, some features (residues on HA protein) in the input layer were observed to take part in multiple active nodes, such as residue 189, 145 and 156, which were also reported to mostly determine the antigenic variation of influenza A(H3N2) viruses. Overall,this work is not only useful for rapidly identifying antigenic variants in influenza prevention, but also an interesting attempt in inferring the mechanisms of biological process through analysis of SAE model, which may give some insights into interpretation of the deep learning

基于VMD和时空网络变分自编码器的负荷聚类

为了解决用户用电负荷曲线数据维度高、特征提取困难以及序列存在信号模态混叠的问题,提出了使用变分模态分解(variational modal decomposition,VMD)和改进基于时空网络的变分自编码器(variational auto-encoder,VAE)对电力负荷曲线进行特征提取。通过模态分解得到信号的固有模态,对模态重构得到时序特征较明显的序列信号。再通过长短期记忆网络(long short-term memory network,LSTM)和卷积网络(convolutional neural network,CNN)组成的时空变分自编码器进行潜在特征提取,并构建网络分类器来联合损失优化自编码器模型。最后使用Minibatchkmeans算法聚类并计算聚类中心。使用UCI数据集中葡萄牙居民用电量作为实验数据,通过实验结果表明经模态分解后通过降维再聚类的算法在戴维斯丁堡指数(Davies-Bouldin index,DBI)和轮廓系数(silhouette coefficient,SC)上表现出较好效果。

基于动态规整与改进变分自编码器的异常电池在线检测方法

针对电池生产成组过程中,传统异常检测方法对混入的容量及压差异常电池检测精度低及生产结束后离线异常检测方法效率低等问题,该文提出一种集合长短期记忆变分自编码器与动态时间规整评价的锂电池异常在线检测方法(VAE-LSTM-DTW),实现了异常电池的在线检测,避免了离线异常检测所造成的时间和能源的浪费。该方法首先将长短期记忆网络(LSTM)引入变分自编码器(VAE)模型,训练电池时序数据重构模型;其次,在电池异常检测的度量标准中引入动态时间规整值(DTW),并基于贝叶斯寻优获得最优检测阈值,对每个单体电池重构数据的动态规整值进行异常辨别。实验结果表明,相较该领域传统异常检测方法,VAE-LSTM-DTW模型性能优越,查准率和F1值都得到了较大的提升,具有较高的有效性和实用性。

基于特征解耦的少样本遥感飞机图像增广算法

基于深度学习的遥感飞机图像分类模型依赖多元化、均衡化的数据集进行训练,但由于飞机目标的高动态特性导致其大规模数据采集困难,现有的飞机细粒度数据集往往存在高质量数据有限、样本不均衡、难以覆盖全部场景的问题。图像生成技术作为数据增广的一种方式在提高数据规模方面发挥了重要作用,但传统的图像生成算法依赖大量样本训练,在少样本条件下的遥感细粒度图像生成问题亟待解决。因此,针对飞机目标图像类间相似度高、类内差异性大的特性提出了一种基于特征解耦的小样本图像生成方法FD-VAE,并在FAIR1M-Aircraft和MAR20两个细粒度遥感飞机数据集上进行测试,与多种先进的图像生成方法相比,FD-VAE的生成图像质量评价指标FID和LPIPS有明显改善。一系列定性和定量实验证明了FD-VAE在生成多样性、高质量的飞机细粒度图像方面具有强竞争力。并且,使用FD-VAE增广后数据集训练的ResNet-18分类网络,相比于传统训练方法精度提升2.3个百分点。FD-VAE有效缓解了细粒度飞机图像高质量数据采集困难的问题,并且有助于提升下游深度学习模型的性能上限。

基于变分自编码器利用元素录井数据确定火成岩矿物含量的方法

火成岩由于岩浆类型及冷凝环境的不同导致矿物含量差异大,不同岩性的骨架参数明显不同。确定岩石骨架的矿物含量是评价储层的一项重要工作,在地层岩性划分、骨架参数计算以及沉积环境的研究等方面有着重要的意义。提出了一种火成岩矿物含量预测模型,该模型使用了元素录井得到的17种元素含量数据,基于变分自编码器(Variational AutoEncoder,VAE)方法预测矿物含量并重构元素含量。模型验证结果显示,该模型在数据集中预测平均绝对误差及均方误差小于BP神经网络(反向传播神经网络,Back Propagation Neural Network)、岭回归和支持向量机这3种典型方法。将该模型应用于南海某地区古潜山火成岩井段,应用结果表明,该模型跟典型算法相比具有优越性,同时具有良好的可应用性。

重加权的对抗变分自编码器及其在工业因果效应估计中的应用

反事实预测和选择偏差是因果效应估计中的重大挑战。为对潜在协变量的复杂混杂分布进行有效表征,同时增强反事实预测泛化能力,提出一种面向工业因果效应估计应用的重加权对抗变分自编码器网络(RVAENet)模型。针对混杂分布去偏问题,借鉴域适应思想,采用对抗学习机制对由变分自编码器(VAE)获得的隐含变量进行表示学习的分布平衡;在此基础上,通过学习样本倾向性权重对样本进行重加权,进一步缩小实验组(Treatment)与对照组(Control)样本间的分布差异。实验结果表明,在工业真实场景数据集的两个场景下,所提模型的提升曲线下的面积(AUUC)比TEDVAE(Treatment Effect with Disentangled VAE)分别提升了15.02%、16.02%;在公开数据集上,所提模型的平均干预效果(ATE)和异构估计精度(PEHE)普遍取得最优结果。

变分自编码器在环境噪声消除中的应用研究

针对语音信号中的环境噪声影响问题,研究一种基于变分自编码器(Variational Auto-Encoder,VAE)的噪声消除方法,并进行了相关实验验证。首先,深入研究VAE的基本原理;其次,引入L_2正则化方法优化VAE,以提高模型的泛化能力和健壮性;最后,利用ESC-50和VCTK数据集构建包含环境噪声的语音信号数据集,并在此基础上进行了一系列实验。实验结果表明,所提方法能够有效降低环境噪声对语音信号的影响。

基于深度学习的视频压缩编码技术研究

视频压缩编码技术是提高数字电视中视频传输和存储效率的关键技术。通过分析数字电视中的视频压缩问题,设计一个面向数字电视的视频压缩数据传输框架,阐述基于变分自编码器(Variational Auto-Encoders,VAE)的视频压缩方法,利用UVG数据集在MATLAB平台上进行实验。实验结果显示,所提方法在视频压缩和重建上取得了较好的效果,验证了其在视频压缩编码领域的有效性和应用潜力。

基于生成对抗网络的事件描述生成

在基于事件的社会网络(EBSN)中,自动生成社交事件(Social Event)的事件描述(Event Description)供组织者参考,从而有效避免描述贫乏、描述过度、精准度低的问题,易于形成丰富、准确、高吸引力的事件描述。为了自动生成与真实事件描述足够相似的文本,提出了一种生成对抗网络(GAN)模型GAN_PG来生成事件描述。GAN_PG模型中的生成模型(Generator)采用变分自编码器(VAE),判别模型(Discriminator)采用带门控循环单元(GRU)的神经网络。模型训练时借鉴了强化学习中的策略梯度(PG)下降,并通过设计合理的奖励函数来训练生成器生成事件描述。实验结果表明,设计的模型生成事件描述的BLEU-4值达到了0.67,证明了提出的事件描述生成模型GAN_PG可以无监督地产生与自然语言足够相似的事件描述。

基于孪生变分自编码器的小样本图像分类方法

当前深度学习大都基于大量数据通过构建深层次的网络实现自动识别,但在很多场景中难以获得大量的样本数据。针对这一问题,提出一种基于孪生变分自编码器(siamese variational auto-encoder,S-VAE)的小样本图像分类方法。通过变分自编码器提取原始训练数据的高层语义特征,然后由两个训练好的变分自编码器的编码器部分组建孪生网络的输入结构,最后通过分类器对样本进行识别。变分自编码器可以解决样本数据量少带来的过拟合问题,孪生网络的结构增加了样本数量较少的情况下的训练次数。在Omniglot数据集上进行的实验结果表明:本方法与原始孪生神经网络相比正确率平均提高了3.1%,模型收敛速度更快,证明了孪生变分自编码器能够较好地完成小样本数据分类任务。