Biblio

This paper designs a data anomaly detection method for power grid data centers. The method uses cloud computing architecture to realize the storage and calculation of large amounts of data from power grid data centers. After that, the STL decomposition method is used to decompose the grid data, and then the decomposed residual data is used for anomaly analysis to complete the detection of abnormal data in the grid data. Finally, the feasibility of the method is verified through experiments.

Facial expression recognition (FER) for emotion inference has become one of the most important research fields in human-computer interaction. Existing study on FER mainly focuses on visible images, whereas varying lighting conditions may influence their performances. Recent studies have demonstrated the advantages of infrared thermal images reflecting the temperature distributions, which are robust to lighting changes. In this paper, a novel infrared image sequence based FER method is proposed using spatiotemporal feature analysis and deep Boltzmann machines (DBM). Firstly, a dense motion field among infrared image sequences is generated using optical flow algorithm. Then, PCA is applied for dimension reduction and a three-layer DBM structure is designed for final expression classification. Finally, the effectiveness of the proposed method is well demonstrated based on several experiments conducted on NVIE database.