Inspired by the recent success of deep learning in multiscale information encoding, we introduce a variational autoencoder (VAE) based semi-supervised method for detection of faulty traffic data, which is cast as a classification problem. Continuous wavelet transform (CWT) is applied to the time series of traffic volume data to obtain rich features embodied in time-frequency representation, followed by a twin of VAE models to separately encode normal data and faulty data. The resulting multiscale dual encodings are concatenated and fed to an attention-based classifier, consisting of a self-attention module and a multilayer perceptron. For comparison, the proposed architecture is evaluated against five different encoding schemes, including (1) VAE with only normal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both normal and faulty data encodings, but without attention module in the classifier, (4) siamese encoding, and (5) cross-vision transformer (CViT) encoding. The first four encoding schemes adopted the same convolutional neural network (CNN) architecture while the fifth encoding scheme follows the transformer architecture of CViT. Our experiments show that the proposed architecture with the dual encoding scheme, coupled with attention module, outperforms other encoding schemes and results in classification accuracy of 96.4%, precision of 95.5%, and recall of 97.7%.
翻译:由于在多级信息编码中深层学习最近的成功,我们引入了一种基于自留模块和多层感应器的基于半监督的变式自动编码器(VAE),用于检测错误的交通数据,这是一个分类问题。对流量数据的时间序列应用连续波变换(CWT),以获取在时间-频率表示法中体现的丰富特征,随后是两个VAE模型,分别编码正常数据和错误数据。由此产生的多级双级双重编码被混为一体,并提供给一个基于关注的分类器,其中包括一个自留模块和一个多层感应器。为比较起见,对拟议的结构按照五个不同的编码方法进行评估,包括:(1) VAE,仅进行正常数据编码,(2) VAE,仅进行错误数据编码,(3) VAE,同时进行正常和错误数据编码,但没有在分类器分类器中进行注意模块,(4) 硅编码,(5) 跨视图变码变码变码器编码器编码。前四个编码系统采用了相同的神经网络(CNN) 结构,而第五级编码系统则进行精确化计划,同时进行我们系统变码化的CIBIL5的系统结构结构的变校正结构。