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This paper concerns static output feedback stabilization of polytopic discrete LTI systems. The previous related studies were mainly based on LMI approaches which are naturally conservative. In this paper, a novel design algorithm is presented that iteratively partitions a primary design space to subspaces. Then, by assessing stabilizability status of each generated subspace, the algorithm determines the total stabilizable parts and removes the undesired parts of the design space.

A high level of monitoring is necessary for the safety and product quality of the electrical fused magnesia furnace (EFMF). In this paper, a monitoring method based on latent subspace for EFMF is proposed to fully mine the effective information of multi-source heterogeneous data in the process. By minimizing the distance of different types of data in the subspace, the corresponding projection matrix is obtained. Then the data is projected into the obtained subspace to estimate whether fault occurs.In summary, the main contributions of this paper are threefold.

Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately.

Most text-simplification systems require an indicator of the complexity of the words. The prevalent approaches to word difficulty prediction are based on manual feature engineering. Using deep learning based models are largely left unexplored due to their comparatively poor performance. We have explored the use of one of such in predicting the difficulty of words. We have treated the problem as a binary classification problem. We have trained traditional machine learning models and evaluated their performance on the task.