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.

Dissolution processes, hydrolysis and condensation reactions during geopolymer synthesis: Part I—Low Si/Al ratio systems

Abstract: Initial geopolymeric reaction processes governing dissolution of solid aluminosilicate particles in alkali solutions have been investigated using conventional experimental techniques, and the data analysed by speciation predictions of the partial charge model (PCM). For metakaolin powders activated with 5.0 M NaOH, solid-state nuclear magnetic resonance (NMR) spectra disclose the existence of monomeric [Al(OH)4]− species after two hours of dissolution, consistent with PCM predictions. However, no equivalent monomeric silicate species were observed for 5.0–10.0 M NaOH activator solutions characteristic of systems with nominal Si/Al ≤ 1. The apparent absence of monomeric silicate species suggest rapid condensation of silicate units with [Al(OH)4]− to generate aluminosilicate species, as indicated by the evolution of the shoulder at around −87 ppm in the 29Si NMR spectra. Of the two possible stable silicate species [SiO2(OH)2]2− and [SiO(OH)3]−, the latter appears most likely to condense with [Al(OH)4]− to produce aluminosilicate oligomers, from which larger oligomers subsequently form through further condensation with [Al(OH)4]− leading to a gradual build up of aluminosilicate networks and a lowering of system alkalinity. This dissolution and hydrolysis sequence at the early stages of synthesis suggests a reaction path wholly consistent with predictions of the partial charge model.

Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels

Abstract: While deep neural networks (DNN) based single image super-resolution (SISR) methods are rapidly gaining popularity, they are mainly designed for the widely-used bicubic degradation, and there still remains the fundamental challenge for them to super-resolve low-resolution (LR) image with arbitrary blur kernels. In the meanwhile, plug-and-play image restoration has been recognized with high flexibility due to its modular structure for easy plug-in of denoiser priors. In this paper, we propose a principled formulation and framework by extending bicubic degradation based deep SISR with the help of plug-and-play framework to handle LR images with arbitrary blur kernels. Specifically, we design a new SISR degradation model so as to take advantage of existing blind deblurring methods for blur kernel estimation. To optimize the new degradation induced energy function, we then derive a plug-and-play algorithm via variable splitting technique, which allows us to plug any super-resolver prior rather than the denoiser prior as a modular part. Quantitative and qualitative evaluations on synthetic and real LR images demonstrate that the proposed deep plug-and-play super-resolution framework is flexible and effective to deal with blurry LR images.

Hierarchical Ensemble of Global and Local Classifiers for Face Recognition

Abstract: In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.