Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Fog Orchestration for Internet of Things Services

Abstract: Large-scale Internet of Things (IoT) services such as healthcare, smart cities, and marine monitoring are pervasive in cyber-physical environments strongly supported by Internet technologies and fog computing. Complex IoT services are increasingly composed of sensors, devices, and compute resources within fog computing infrastructures. The orchestration of such applications can be leveraged to alleviate the difficulties of maintenance and enhance data security and system reliability. However, efficiently dealing with dynamic variations and transient operational behavior is a crucial challenge within the context of choreographing complex services. Furthermore, with the rapid increase of the scale of IoT deployments, the heterogeneity, dynamicity, and uncertainty within fog environments and increased computational complexity further aggravate this challenge. This article gives an overview of the core issues, challenges, and future research directions in fog-enabled orchestration for IoT services. Additionally, it presents early experiences of an orchestration scenario, demonstrating the feasibility and initial results of using a distributed genetic algorithm in this context.

Off-Policy Reinforcement Learning for $ H_\infty $ Control Design

Abstract: The $H_\infty $ control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear $ H_\infty $ control problem can be transformed into solving the so-called Hamilton–Jacobi–Isaacs (HJI) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, model-based approaches cannot be used for approximately solving HJI equation, when the accurate system model is unavailable or costly to obtain in practice. To overcome these difficulties, an off-policy reinforcement leaning (RL) method is introduced to learn the solution of HJI equation from real system data instead of mathematical system model, and its convergence is proved. In the off-policy RL method, the system data can be generated with arbitrary policies rather than the evaluating policy, which is extremely important and promising for practical systems. For implementation purpose, a neural network (NN)-based actor-critic structure is employed and a least-square NN weight update algorithm is derived based on the method of weighted residuals. Finally, the developed NN-based off-policy RL method is tested on a linear F16 aircraft plant, and further applied to a rotational/translational actuator system.

Ultrathin amorphous cobalt–vanadium hydr(oxy)oxide catalysts for the oxygen evolution reaction

Abstract: Cost efficient and long-term stable catalysts are in great demand for the oxygen evolution reaction (OER), a key process involved in water splitting cells and metal–air batteries. Here, we demonstrate that the ultrathin amorphous cobalt–vanadium hydr(oxy)oxide we synthesized is a highly promising electrocatalytic material for the OER with a low overpotential of 0.250 V (even lower down to 0.215 V when supported on Au foam) at 10 mA cm−2 and a long stable operation time (170 h) in alkaline media. In combination with in situ X-ray absorption spectral characterization and first-principles simulations, we reveal that the ultrathin, amorphous and alloyed structural characteristics have enabled its facile transformation to the desirable active phase, leading to a dramatically enhanced catalytic activity. Our finding highlights the remarkable advantages of the two-dimensional amorphous material and sheds new light on the design of high-performance electrocatalysts.

Fly‐Eye Inspired Superhydrophobic Anti‐Fogging Inorganic Nanostructures

Abstract: Fly-eye bio-inspired inorganic nanostructures are synthesized via a two-step self-assembly approach, which have low contact angle hysteresis and excellent anti-fogging properties, and are promising candidates for anti-freezing/fogging materials to be applied in extreme and hazardous environments