Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Control theory & Catalysis. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Robust Stability for Uncertain Delayed Fuzzy Hopfield Neural Networks With Markovian Jumping Parameters

Abstract: This paper is concerned with the problem of the robust stability of nonlinear delayed Hopfield neural networks (HNNs) with Markovian jumping parameters by Takagi-Sugeno (T-S) fuzzy model. The nonlinear delayed HNNs are first established as a modified T-S fuzzy model in which the consequent parts are composed of a set of Markovian jumping HNNs with interval delays. Time delays here are assumed to be time-varying and belong to the given intervals. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality approach, stability conditions are proposed in terms of the upper and lower bounds of the delays. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.

Novel effect of SO2 on the SCR reaction over CeO2: Mechanism and significance

Abstract: Ceria (CeO2) showed a poor activity for the selective catalytic reduction (SCR) of NO with NH3, while CeO2 showed an excellent SCR activity in the presence of SO2 at 300–500 °C. The promotion of SO2 on the SCR reaction over CeO2 was mainly due to the sulfation of CeO2. The SCR reaction over CeO2 and that over sulfated CeO2 both followed the Eley–Rideal mechanism (i.e. the reaction between activated NH3 with gaseous NO). Meanwhile, the catalytic oxidization of NH3 to NO could simultaneously happen during the SCR reaction, resulting in a drop of NOx conversion at high temperatures. The adsorption of NH3 on CeO2 was obviously promoted after the sulfation, resulting in an obvious promotion of the Eley–Rideal mechanism. Meanwhile, the adsorption sites for NH2 adsorption and the oxidization agents for NH2 oxidization on CeO2 were separated after the sulfation. Thus, the probability of the collision between NH2 and Ce4+ on sulfated CeO2 was much less than that on CeO2, resulting in an obvious repression of the catalytic oxidization of NH3 to NO. As a result, the SCR activity of sulfated CeO2 was much better than that over CeO2.

Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection

Abstract: Many pattern analysis and data mining problems have witnessed high-dimensional data represented by a large number of features, which are often redundant and noisy. Feature selection is one main technique for dimensionality reduction that involves identifying a subset of the most useful features. In this paper, a novel unsupervised feature selection algorithm, named clustering-guided sparse structural learning (CGSSL), is proposed by integrating cluster analysis and sparse structural analysis into a joint framework and experimentally evaluated. Nonnegative spectral clustering is developed to learn more accurate cluster labels of the input samples, which guide feature selection simultaneously. Meanwhile, the cluster labels are also predicted by exploiting the hidden structure shared by different features, which can uncover feature correlations to make the results more reliable. Row-wise sparse models are leveraged to make the proposed model suitable for feature selection. To optimize the proposed formulation, we propose an efficient iterative algorithm. Finally, extensive experiments are conducted on 12 diverse benchmarks, including face data, handwritten digit data, document data, and biomedical data. The encouraging experimental results in comparison with several representative algorithms and the theoretical analysis demonstrate the efficiency and effectiveness of the proposed algorithm for feature selection.