University of Science and Technology Beijing

About: University of Science and Technology Beijing is a education organization based out in Beijing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 41558 authors who have published 44473 publications receiving 623229 citations. The organization is also known as: Beijing Steel and Iron Institute.

Urban computing with taxicabs

Abstract: Urban computing for city planning is one of the most significant applications in Ubiquitous computing. In this paper we detect flawed urban planning using the GPS trajectories of taxicabs traveling in urban areas. The detected results consist of 1) pairs of regions with salient traffic problems and 2) the linking structure as well as correlation among them. These results can evaluate the effectiveness of the carried out planning, such as a newly built road and subway lines in a city, and remind city planners of a problem that has not been recognized when they conceive future plans. We conduct our method using the trajectories generated by 30,000 taxis from March to May in 2009 and 2010 in Beijing, and evaluate our results with the real urban planning of Beijing.

Atomically Dispersed Molybdenum Catalysts for Efficient Ambient Nitrogen Fixation

Abstract: NH3 synthesis by the electrocatalytic N2 reduction reaction (NRR) under ambient conditions is an appealing alternative to the currently employed industrial method-the Haber-Bosch process-that requires high temperature and pressure. We report single Mo atoms anchored to nitrogen-doped porous carbon as a cost-effective catalyst for the NRR. Benefiting from the optimally high density of active sites and hierarchically porous carbon frameworks, this catalyst achieves a high NH3 yield rate (34.0±3.6 μg NH 3 h-1 mgcat. -1 ) and a high Faradaic efficiency (14.6±1.6 %) in 0.1 m KOH at room temperature. These values are considerably higher compared to previously reported non-precious-metal electrocatalysts. Moreover, this catalyst displays no obvious current drop during a 50 000 s NRR, and high activity and durability are achieved in 0.1 m HCl. The findings provide a promising lead for the design of efficient and robust single-atom non-precious-metal catalysts for the electrocatalytic NRR.

Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning

Abstract: This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there is no need to get the prior knowledge about the uncertainty and a sufficient amount of observed data. Also, impedance learning is introduced to tackle the interaction between the robot and its environment, so that the robot follows a desired destination generated by impedance learning. A barrier Lyapunov function is used to address the effect of state constraints. With the proposed control, the stability of the closed-loop system is achieved via Lyapunov’s stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty. Some simulation studies are carried out to illustrate the effectiveness of the proposed scheme.