Pavement condition detection is beneficial to road maintenance and driving experience. Acceleration sensors of smart phones can provide an economical and ubiquitous way to gather pavement condition data. A pavement condition detection method is proposed based on acceleration sensor data of smart phones, which incorporates time-frequency features into capsule networks. The method well addresses the problems of low accuracy caused by the length change of time series, and the high dimensionality of sensor data. Experiments show that the method proposed outperforms the representative methods in accuracy, precision and F1 scores. Especially, it has an improvement in F1 score up to 31.62% compared with the benchmark methods.

The outlier detection of high-dimensional data is still of challenge. The performance of existing unsupervised approaches will be affected with the increase of outliers in a dataset. The stacked autoencoder and GMM are introduced to the detection of outliers, and an approach termed SAGMM is proposed. The stacked autoencoder can reduce the reconstruction error of observations, and the GMM determines the outliers based on the mixture distributions of observations obtained in model training. Experiments on public datasets show that the proposed approach SAGMM outperforms the similar approaches in precision, and has a good balance between the precision and recall rate, since it improves the F1 scores compared with them.

The heating surface temperature controlling of thermal boilers are critical to safe production, energy saving and emission reduction. With the background of temperature prediction of heating surfaces in thermal boilers, the paper proposes a novel time-series clustering approach at first. Considering time series as arbitrary signals, features extracted from their Marginal Spectrums based on Hilbert Huang Transform is introduced to the clustering. From the proposed time-series clustering approach, a Spatio-temporal clustering approach to enabling local heating surface partitioning is derived. The temperature of the heating surfaces partitioned is then predicted using an LSTM model depending on multiple time-series related to the work conditions of a boiler. The proposed time-series clustering is compared with the traditional approaches on public datasets, and shows great advantages, and the temperature prediction of local heating surfaces of thermal boilers in practice also verify that the proposed approaches are effective.

Anomaly detection has drawn public attentions in past decades. However, in a high-dimensional sparse data space, anomaly detection still faces big challenges. In this paper, the Generative Adversarial Network (GAN) combined with Ensemble Learning is introduced to anomaly detection in high-dimensional sparse data. On one hand, the generator of GAN can produce noise data to avoid the data space to be too sparse based on the potential data distribution patterns. On the other hand, the exchanges of the pairing of generators and discriminators can enable the model proposed to learn complex distribution of the data, which may be composed of some various distributions, and to avoid the training process to drop into over-fitting to some extent. Experiments on public datasets show that the proposed approach can improve AUC by 7% compared with traditional GAN based approaches, and by 7.5% to 21.8% compared with other representative anomaly detection approaches.