In real industrial processes, both outliers and missing data are very common. Owing to the assumption that the data sampled from a normal process follow the Gaussian distribution, the regular datadriven process monitoring methods, such as the probabilistic partial least square (PPLS) method and the probabilistic principal component analysis method, are sensitive to outliers. By introducing heavy-tailed t distribution instead of Gaussian distribution to capture the distribution of normal data, the robust data-driven method can significantly reduce the influence of outliers on the development of the model. To reduce the influence of missing data, this paper proposes a process modeling and monitoring method with incomplete data based on the robust PPLS method. In the proposed method, to use more useful information in modeling, incomplete data along with complete data are employed in the parameter estimation using the maximum likelihood method; according to the robust PPLS model and the Bayes' rule, the distributions of latent variables and missing data are derived, and subsequently, the expectation-maximization algorithm is used to achieve the parameter estimation. In addition, based on the conditional distribution of missing data, two monitoring indices are developed to evaluate the deviation of latent variables and residuals. A simulation case illustrates the application of the proposed method, and the results of application demonstrate its efficacy.

Simultaneous localization and mapping (SLAM) is considered to be an important way for some smart devices to perform automatic path planning, and many successful SLAM systems have been developed in the past few years. Most existing approaches rely heavily on static world assumptions, and such strong assumptions limit the application of most vSLAM (visual SLAM) in complex dynamic reality environments, where dynamic objects often lead to incorrect data association in tracking, which reduces the overall accuracy and robustness of the system and causes tracking crashes. The dynamic objects in the map may change over time; thus, distinguishing dynamic information in a scene is challenging. In order to solve the interference problem of dynamic objects, most point-based visual odometry algorithms have concentrated on feature matching or direct pixel intensity matching, disregarding an ordinary but crucial image entity: geometric information. In this article, we put forward a novel visual odometry algorithm based on dynamic point detection methods called geometric prior and constraints. It removes the moving objects by combining the spatial geometric information of the image and depends on the remaining features to estimate the position of the camera. To the best of our knowledge, our proposed algorithm achieves superior performance over existing methods on a variety of public datasets.

Dynamic Modeling of Biotechnical Process Based on Online Support Vector Machine