Henan University of Technology

About: Henan University of Technology is a education organization based out in Zhengzhou, China. It is known for research contribution in the topics: Catalysis & Chemistry. The organization has 7648 authors who have published 6503 publications receiving 73067 citations. The organization is also known as: Hénán Gōngyè Dàxué.

Semisupervised Feature Extraction of Hyperspectral Image Using Nonlinear Geodesic Sparse Hypergraphs

Abstract: Recently, the sparse representation (SR)-based graph embedding method has been extensively used in feature extraction (FE) tasks, but it is hard to reveal the complex manifold structure and multivariate relationship of samples in the hyperspectral image (HSI). Meanwhile, the small size sample problem in HSI data also limits the performance of the traditional SR approach. To tackle this problem, this article develops a new semisupervised FE algorithm called a geodesic-based sparse manifold hypergraph (GSMH). The presented method first utilizes the geodesic distance to measure the nonlinear similarity between samples lying on manifold space and further constructs the manifold neighborhood of each sample. Then, a geodesic-based neighborhood SR (GNSR) model is designed to explore the multivariate sparse correlations of different manifold neighborhoods. Considering the multivariate sparse manifold correlations among samples, a pair of semisupervised hypergraphs (HGs) is constructed to effectively incorporate the labeled and unlabeled training information in the embedding process and obtain the nonlinear discriminative feature representation for HSI. Experimental results on three HSI datasets indicate that the proposed method not only achieves satisfying FE performance with limited labeled training samples but also shows superiority compared with other state-of-the-art methods.

Superhydrophobic and superoleophilic nanoparticle film: synthesis and reversible wettability switching behavior.

Abstract: The present work describes a one-step facile spray deposition process for the fabrication of superhydrophobic and superoleophilic nanoparticle film. The film shows fast response wettability transition between superhydrophobicity and hydrophilicity. The reversible superhydrophobicity to hydrophilicity switching can be easily carried out by adjusting the temperature. The film also demonstrates oil uptake ability and can selectively adsorb oil floating on water surface. Furthermore, the film surface shows the antifouling performance for organic solvents, which can self-remove the organic solvents layer and recover its superhydrophobic behavior. The advantage of the present approach is that the damaged film can be easily repaired by spraying again.

Distributed Position-Based Consensus of Second-Order Multiagent Systems With Continuous/Intermittent Communication

Abstract: This paper considers the position-based consensus in a network of agents with double-integrator dynamics and directed topology. Two types of distributed observer algorithms are proposed to solve the consensus problem by utilizing continuous and intermittent position measurements, respectively, where each observer does not interact with any other observers. For the case of continuous communication between network agents, some convergence conditions are derived for reaching consensus in the network with a single constant delay or multiple time-varying delays on the basis of the eigenvalue analysis and the descriptor method. When the network agents can only obtain intermittent position data from local neighbors at discrete time instants, the consensus in the network without time delay or with nonuniform delays is investigated by using the Wirtinger’s inequality and the delayed-input approach. Numerical examples are given to illustrate the theoretical analysis.

Comparison of Graphene-Based Transverse Magnetic and Electric Surface Plasmon Modes

Abstract: Dispersion properties and field distributions of graphene supported transverse magnetic (TM) and transverse electric (TE) surface plasmon (SP) modes in air-graphene-SiO2-Si structures have been investigated. The results show that graphene-based TM (TE) SPs are bound (lossy) modes, which decay into the air in the range of tens of micrometers (several thousand micrometers). In addition, when the thickness of the SiO2 layer is in the range of 200-300 nm, the influence of the Si substrate on the dispersion property is significant (negligible) for the TM (TE) modes. Furthermore, the effective indexes of the graphene TM (TE) modes increase with the increase (decrease) of the frequency. Compared with the traditional metal-based structures, graphene-based TM mode exhibits a better confinement but with a larger loss. The presented results are useful for the design of compact graphene-based optoelectronic devices.

Event-Based Dissipative Analysis for Discrete Time-Delay Singular Jump Neural Networks

Abstract: This paper investigates the event-triggered dissipative filtering issue for discrete-time singular neural networks with time-varying delays and Markovian jump parameters. Via event-triggered communication technique, a singular jump neural network (SJNN) model of network-induced delays is first given, and sufficient criteria are then provided to guarantee that the resulting augmented SJNN is stochastically admissible and strictly stochastically dissipative (SASSD) with respect to $(\mathcal {X}_{\iota },\mathcal {Y}_{\iota },\mathcal {Z}_{\iota },\delta)$ by using slack matrix scheme. Furthermore, employing filter equivalent technique, codesigned filter gains, and event-triggered matrices are derived to make sure that the augmented SJNN model is SASSD with respect to $(\mathcal {X}_{\iota },\mathcal {Y}_{\iota },\mathcal {Z}_{\iota },\delta)$ . An example is also given to illustrate the effectiveness of the proposed method.