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.

Stretchable, Washable, and Ultrathin Triboelectric Nanogenerators as Skin-Like Highly Sensitive Self-Powered Haptic Sensors

Abstract: Accompanying the boom in multifunctional wearable electronics, flexible, sustainable, and wearable power sources are facing great challenges. Here, a stretchable, washable, and ultrathin skin-inspired triboelectric nanogenerator (SI-TENG) to harvest human motion energy and act as a highly sensitive selfpowered haptic sensor is reported. With the optimized material selections and structure design, the SI-TENG is bestowed with some merits, such as stretchability (≈800%), ultrathin (≈89 μm), and light-weight (≈0.23 g), which conformally attach on human skin without disturbing its contact. A stretchable composite electrode, which is formed by homogenously intertwining silver nanowires (AgNWs) with thermoplastic polyurethane (TPU) nanofiber networks, is fabricated through synchronous electrospinning of TPU and electrospraying of AgNWs. Based on the triboelectrification effect, the open-circuit voltage, short-circuit current, and power density of the SI-TENG with a contact area of 2 × 2 cm2 and an applied force of 8 N can reach 95 V, 0.3 μA, and 6 mW m−2, respectively. By integrating the signalprocessing circuits, the SI-TENG with excellent energy harvesting and selfpowered sensing capability is demonstrated as a haptic sensor array to detect human actions. The SI-TENG exhibits extensive applications in the fields of human–machine interface and security systems.

Computation-Efficient Offloading and Trajectory Scheduling for Multi-UAV Assisted Mobile Edge Computing

Abstract: The emergence of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) is of great significance for the prospective development of Internet of Things (IoT). The additional computation capability and extensive network coverage provide energy-limited smart mobile devices (SMDs) with more opportunities to experience diverse intelligent applications. In this paper, a computation efficiency maximization problem is formulated in a multi-UAV assisted MEC system, where both computation bits and energy consumption are considered. Based on the partial computation offloading mode, user association, allocation of central processing unit (CPU) cycle frequency, power and spectrum resources, as well as trajectory scheduling of UAVs are jointly optimized. Due to the non-convexity of the problem and the coupling among variables, we propose an iterative optimization algorithm with double-loop structure to find the optimal solution. Simulation results demonstrate that the proposed algorithm can obtain higher computation efficiency than baseline schemes while guaranteeing the quality of computation service.

Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Convergence Analysis

Abstract: In this paper, convergence properties are established for the newly developed discrete-time local value iteration adaptive dynamic programming (ADP) algorithm. The present local iterative ADP algorithm permits an arbitrary positive semidefinite function to initialize the algorithm. Employing a state-dependent learning rate function, for the first time, the iterative value function and iterative control law can be updated in a subset of the state space instead of the whole state space, which effectively relaxes the computational burden. A new analysis method for the convergence property is developed to prove that the iterative value functions will converge to the optimum under some mild constraints. Monotonicity of the local value iteration ADP algorithm is presented, which shows that under some special conditions of the initial value function and the learning rate function, the iterative value function can monotonically converge to the optimum. Finally, three simulation examples and comparisons are given to illustrate the performance of the developed algorithm.