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This article develops a brand new technique to perform safety monitoring using images for a type of complex industrial processes. The new safety monitoring technique is developed with the aid of a modified nonnegative matrix factorization (NMF) algorithm that is called multiple-centroid strictly convex nonnegative matrix factorization (MCSCNMF). Specifically, MCSCNMF is designed deliberately in an all-new manner such that it can learn more accurate centroids for each group of samples than the existing NMF-like algorithms. Moreover, MCSCNMF takes advantage of multiple centroids instead of single centroid to describe the complicated distribution of each type of samples. Both properties give MCSCNMF a powerful clustering performance and make it beneficial for developing high-performance safety monitoring techniques than the existing NMF-like algorithms. We compare different types of monitoring methods in terms of false negative, false positive, and average approximation error in an experiment on a steel coil painting process. Comparison results demonstrate that the MCSCNMF-based monitoring technique can obtain the best experimental results compared with other monitoring methods all the time. [ABSTRACT FROM AUTHOR]

In Part I of the study of the tensor deflation for CANDECOMP/PARAFAC, we have shown that the rank-1 tensor deflation is applicable under some conditions. Part II of the study presents several initialization algorithms suitable for the algorithm proposed in Part I. In addition, Part II contains an algorithm for the case when one or more factor matrices in the estimated model is constrained to be orthogonal. Finally, Part II provides an error analysis of the tensor deflation algorithm, which shows that there is a marginal loss of accuracy of the deflation algorithm compared to the ordinary CP decomposition. [ABSTRACT FROM PUBLISHER]

Abstract: Fast and accurate phase equilibrium calculations have been a hot research topic during the past decades due to the increased computational effort they impose in process simulation. Reduction methods have attracted significant interest as a means to accelerate calculations by decreasing the problem''s dimensionality. However, when many binary interaction parameters (BIPs) exhibit non-zero values, reduction methods offer limited advantage and the introduction of approximation error is inevitable. In this work a new method is presented for replacing the conventional spectral decomposition basis vectors by new ones so that the approximation error of the energy parameter is minimized instead of that of BIP matrix. The new reduced variables set leads to improved flash calculations accuracy, thus allowing them to be performed at a given accuracy level using fewer reduced variables compared to the conventional approach. Moreover, it is shown that the formulation of the proposed matrix decomposition method is a generalization of the spectral analysis one. The extension of the proposed method to temperature-dependent BIPs is also discussed. A set of examples demonstrates the efficiency of the proposed method. The method can be readily applied to all existing phase behavior algorithms within the reduced variables framework as it intervenes solely on the treatment of the BIP matrix. [Copyright &y& Elsevier]