Dalian University of Technology

About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.

UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells

Abstract: In future mobile networks, it is difficult for static base stations (BSs) to support the rapidly increasing data services, especially for cell-edge users. Unmanned aerial vehicle (UAV) is a promising method that can assist BSs to offload the data traffic, due to its high mobility and flexibility. In this correspondence, we focus on the UAV trajectory at the edges of three adjacent cells to offload traffic for BSs. In the proposed scheme, the sum rate of UAV-served edge users is maximized subject to the rate requirements for all the users, by optimizing the UAV trajectory in each flying cycle. The optimization is a mixed-integer nonconvex problem, which is difficult to solve. Thus, it is transformed into two convex problems, and an iterative algorithm is proposed to solve it by optimizing the UAV trajectory and edge user scheduling alternately. Simulation results are presented to show the effectiveness of the proposed scheme.

Al2O3 Nanosheets Rich in Pentacoordinate Al(3+) Ions Stabilize Pt-Sn Clusters for Propane Dehydrogenation.

Abstract: In heterogeneous catalysis, supports play a crucial role in modulating the geometric and electronic structure of the active metal phase for optimizing the catalytic performance. A γ-Al2O3 nanosheet that contains 27% pentacoordinate Al(3+) sites can nicely disperse and stabilize raft-like Pt-Sn clusters as a result of strong interactions between metal and support. Consequently, there are strong electronic interactions between the Pt and Sn atoms, resulting in an increase in the electron density of the Pt sites. When used in the propane dehydrogenation reaction, this catalyst displayed an excellent specific activity for propylene formation with >99% selectivity, and superior anti-coking and anti-sintering properties. Its exceptional ability to maintain the high activity and stability at ultrahigh space velocities further showed that the sheet construction of the catalyst facilitated the kinetic transfer process.

Engineering active sites on hierarchical transition bimetal oxides/sulfides heterostructure array enabling robust overall water splitting

Abstract: Rational design of the catalysts is impressive for sustainable energy conversion. However, there is a grand challenge to engineer active sites at the interface. Herein, hierarchical transition bimetal oxides/sulfides heterostructure arrays interacting two-dimensional MoOx/MoS2 nanosheets attached to one-dimensional NiOx/Ni3S2 nanorods were fabricated by oxidation/hydrogenation-induced surface reconfiguration strategy. The NiMoOx/NiMoS heterostructure array exhibits the overpotentials of 38 mV for hydrogen evolution and 186 mV for oxygen evolution at 10 mA cm−2, even surviving at a large current density of 500 mA cm−2 with long-term stability. Due to optimized adsorption energies and accelerated water splitting kinetics by theory calculations, the assembled two-electrode cell delivers the industrially relevant current densities of 500 and 1000 mA cm−2 at record low cell voltages of 1.60 and 1.66 V with excellent durability. This research provides a promising avenue to enhance the electrocatalytic performance of the catalysts by engineering interfacial active sites toward large-scale water splitting. While water splitting is an appealing carbon-neutral strategy for renewable energy generation, there is a need to develop new active, cost-effective catalysts. Here, authors prepare a nickel-molybdenum oxide/sulfide heterojunctions as bifunctional H2 and O2 evolution electrocatalysts.

Chaotic Time Series Prediction Based on a Novel Robust Echo State Network

Abstract: In this paper, a robust recurrent neural network is presented in a Bayesian framework based on echo state mechanisms. Since the new model is capable of handling outliers in the training data set, it is termed as a robust echo state network (RESN). The RESN inherits the basic idea of ESN learning in a Bayesian framework, but replaces the commonly used Gaussian distribution with a Laplace one, which is more robust to outliers, as the likelihood function of the model output. Moreover, the training of the RESN is facilitated by employing a bound optimization algorithm, based on which, a proper surrogate function is derived and the Laplace likelihood function is approximated by a Gaussian one, while remaining robust to outliers. It leads to an efficient method for estimating model parameters, which can be solved by using a Bayesian evidence procedure in a fully autonomous way. Experimental results show that the proposed method is robust in the presence of outliers and is superior to existing methods.