Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

Delay-dependent robust H/sub /spl infin// and L/sub 2/-L/sub /spl infin// filtering for a class of uncertain nonlinear time-delay systems

Abstract: This note presents delay-dependent robust H/sub /spl infin// and L/sub 2/-L/sub /spl infin// filter designs for a class of nonlinear systems with multiple time-varying delays in the state and parameter uncertainties residing in a polytope. The nonlinearities are assumed to satisfy global Lipschitz conditions. Attention is focused on the design of robust full-order and reduced-order filters guaranteeing a prescribed noise attenuation level in an H/sub /spl infin// or L/sub 2/-L/sub /spl infin// sense. The admissible filters can be obtained from the solution of convex optimization problems in terms of linear matrix inequalities, which can be solved via efficient interior-point algorithms.

Hyperspectral Image Classification With Deep Learning Models

Abstract: Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.

Single-Site Active Iron-Based Bifunctional Oxygen Catalyst for a Compressible and Rechargeable Zinc-Air Battery.

Abstract: The exploitation of a high-efficient, low-cost, and stable non-noble-metal-based catalyst with oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) simultaneously, as air electrode material for a rechargeable zinc–air battery is significantly crucial Meanwhile, the compressible flexibility of a battery is the prerequisite of wearable or/and portable electronics Herein, we present a strategy via single-site dispersion of an Fe–Nx species on a two-dimensional (2D) highly graphitic porous nitrogen-doped carbon layer to implement superior catalytic activity toward ORR/OER (with a half-wave potential of 086 V for ORR and an overpotential of 390 mV at 10 mA·cm–2 for OER) in an alkaline medium Furthermore, an elastic polyacrylamide hydrogel based electrolyte with the capability to retain great elasticity even under a highly corrosive alkaline environment is utilized to develop a solid-state compressible and rechargeable zinc–air battery The creatively developed battery has a low charge–discha

Surface hydroxyl groups of synthetic α-FeOOH in promoting OH generation from aqueous ozone: Property and activity relationship

Abstract: This work investigated the relationship between the property of the surface hydroxyl groups of hydroxylated synthetic α-FeOOH and their catalytic activity in promoting hydroxyl radical ( OH) generation from aqueous ozone. Nitrobenzene was used as an ozone-resistant probe to quantify OH generation. ATR-FTIR analysis reveals that sulfate and phosphate suppressed the catalytic activity of α-FeOOH through substituting its surface hydroxyl groups, which implies that the catalyst surface hydroxyl groups were active sites for promoting OH generation. Compared with other synthetic oxo-hydroxides such as β-FeOOH, γ-FeOOH and γ-AlOOH, α-FeOOH achieved a highest Rc value (i.e., 1.11 × 10−7, molar concentration ratio of OH to O3) in catalytic ozonation. No correlation could be established between the surface hydroxyl density and the OH-promoting activity of the oxo-hydroxides. In contrast, their catalytic activity was found to be reversely related to the IR stretching frequencies of surface hydroxyl groups. The results indicate that not all surface hydroxyl groups of the oxo-hydroxides possessed the same high catalytic activity, but the weak surface MeO–H bonds were favorable sites for promoting OH generation from aqueous ozone. The surface hydroxyl–ozone interaction was thus proposed for the catalyzed OH generation, which can explain why neutral surface hydroxyl species of α-FeOOH was more active than protonated or deprotonated species.