This brief proposes an independent component analysis-principal component analysis (ICA-PCA) integrating with relevance vector machine (RVM) for multivariate process monitoring. Given the fact that the distribution of industrial process variables is mostly non-Gaussian and PCA cannot well deal with the non-Gaussian part. A hybrid ICA-PCA method is proposed to simultaneously extract the non-Gaussian and Gaussian information of multivariate processes. ICA is first used to monitor the non-Gaussian part of the process and then the Gaussian part of the residual process can be extracted using PCA. After feature extraction, a Bayesian-based classifier named RVM is established to make fault detection for the sake of both preventing the chosen of threshold as in traditional method and compensating for the single statistic. The performance of the proposed approach is validated using the Tennessee Eastman process. Simulation results verified the effectiveness of the proposed method.

With the rapid development of the internet technology, a large amount of internet text data can be obtained. The text classification (TC) technology plays a very important role in processing massive text data, but the accuracy of classification is directly affected by the performance of term weighting in TC. Due to the original design of information retrieval (IR), term frequency-inverse document frequency (TF-IDF) is not effective enough for TC, especially for processing text data with unbalanced distributions in internet media reports. Therefore, the variance between the DF value of a particular term and the average of all DFs , namely, the document frequency variance (ADF), is proposed to enhance the ability in processing text data with unbalanced distribution. Then, the normal TF-IDF is modified by the proposed ADF for processing unbalanced text collection in four different ways, namely, TF-IADF, TF-IADF⁺, TF-IADF_norm, and TF-IADF⁺_norm. As a result, an effective model can be established for the TC task of internet media reports. A series of simulations have been carried out to evaluate the performance of the proposed methods. Compared with TF-IDF on state-of-the-art classification algorithms, the effectiveness and feasibility of the proposed methods are confirmed by simulation results.