Beijing University of Technology

About: Beijing University of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Microstructure & Computer science. The organization has 31929 authors who have published 31987 publications receiving 352112 citations. The organization is also known as: Běijīng Gōngyè Dàxué & Beijing Polytechnic University.

Quantum Hilbert Image Scrambling

Abstract: Analogies between quantum image processing (QIP) and classical one indicate that quantum image scrambling (QIS), as important as quantum Fourier transform (QFT), quantum wavelet transform (QWT) and etc., should be proposed to promote QIP. Image scrambling technology is commonly used to transform a meaningful image into a disordered image by permutating the pixels into new positions. Although image scrambling on classical computers has been widely studied, we know much less about QIS. In this paper, the Hilbert image scrambling algorithm, which is commonly used in classical image processing, is carried out in quantum computer by giving the scrambling quantum circuits. First, a modified recursive generation algorithm of Hilbert scanning matrix is given. Then based on the flexible representation of quantum images, the Hilbert scrambling quantum circuits, which are recursive and progressively layered, is proposed. Theoretical analysis indicates that the network complexity scales squarely with the size of the circuit’s input n.

TL-GDBN: Growing Deep Belief Network With Transfer Learning

Abstract: A deep belief network (DBN) is effective to create a powerful generative model by using training data. However, it is difficult to fast determine its optimal structure given specific applications. In this paper, a growing DBN with transfer learning (TL-GDBN) is proposed to automatically decide its structure size, which can accelerate its learning process and improve model accuracy. First, a basic DBN structure with single hidden layer is initialized and then pretrained, and the learned weight parameters are frozen. Second, TL-GDBN uses TL to transfer the knowledge from the learned weight parameters to newly added neurons and hidden layers, which can achieve a growing structure until the stopping criterion for pretraining is satisfied. Third, the weight parameters derived from pretraining of TL-GDBN are further fine-tuned by using layer-by-layer partial least square regression from top to bottom, which can avoid many problems of traditional backpropagation algorithm-based fine-tuning. Moreover, the convergence analysis of the TL-GDBN is presented. Finally, TL-GDBN is tested on two benchmark data sets and a practical wastewater treatment system. The simulation results show that it has better modeling performance, faster learning speed, and more robust structure than existing models. Note to Practitioners —Transfer learning (TL) aims to improve training effectiveness by transferring knowledge from a source domain to target domain. This paper presents a growing deep belief network (DBN) with TL to improve the training effectiveness and determine the optimal model size. Facing a complex process and real-world workflow, DBN tends to require long time for its successful training. The proposed growing DBN with TL (TL-GDBN) accelerates the learning process by instantaneously transferring the knowledge from a source domain to each new deeper or wider substructure. The experimental results show that the proposed TL-GDBN model has a great potential to deal with complex system, especially the systems with high nonlinearity. As a result, it can be readily applicable to some industrial nonlinear systems.

Shaking table test on the seismic failure characteristics of a subway station structure on liquefiable ground

Abstract: SUMMARY In order to investigate the seismic failure characteristics of a structure on the liquefiable ground, a series of shaking table tests were conducted based on a plaster model of a three-story and three-span subway station. The dynamic responses of the structure and ground soil under main shock and aftershock ground motions were studied. The sand boils and waterspouts phenomena, ground surface cracks, and earthquake-induced ground surface settlements were observed in the testing. For the structure, the upward movement, local damage and member cracking were obtained. Under the main shock, there appeared longer liquefaction duration for the ground soil while the pore pressure dissipated slowly. The acceleration amplification effect of the liquefied soil was weakened, and the soil showed a remarkable shock absorption and concentration effect with low frequency component of ground motion. However, under the aftershock, the dissipation of pore pressure in the ground soil became obvious. The peak acceleration of the structure reduced with the buried depth. Dynamic soil pressure on the side wall was smaller in the middle and larger at both ends. The interior column of the model structure was the weakest member. The peak strain and damage degree for both sides of the interior column exhibited an ‘S’ type distribution along the height. Moreover, the seismic response of both ground soil and subway station structure exhibited a remarkable spatial effect. The seismic damage development process and failure mechanism of the structure illustrated in this study can provide references for the engineers and researcher. Copyright © 2013 John Wiley & Sons, Ltd.

Realizing High Thermoelectric Performance in n-Type Highly Distorted Sb-Doped SnSe Microplates via Tuning High Electron Concentration and Inducing Intensive Crystal Defects

Abstract: In this study, a record high figure of merit (ZT) of ≈1.1 at 773 K is reported in n‐type highly distorted Sb‐doped SnSe microplates via a facile solvothermal method. The pellets sintered from the Sb‐doped SnSe microplates show a high power factor of ≈2.4 µW cm−1 K−2 and an ultralow thermal conductivity of ≈0.17 W m−1 K−1 at 773 K, leading a record high ZT. Such a high power factor is attributed to a high electron concentration of 3.94 × 1019 cm−3 via Sb‐enabled electron doping, and the ultralow thermal conductivity derives from the enhanced phonon scattering at intensive crystal defects, including severe lattice distortions, dislocations, and lattice bent, observed by detailed structural characterizations. This study fills in the gaps of fundamental doping mechanisms of Sb in SnSe system, and provides a new perspective to achieve high thermoelectric performance in n‐type polycrystalline SnSe.