Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Active disturbance rejection adaptive control of uncertain nonlinear systems: theory and application

Abstract: This paper proposes an active disturbance rejection adaptive controller for tracking control of a class of uncertain nonlinear systems with consideration of both parametric uncertainties and uncertain nonlinearities by effectively integrating adaptive control with extended state observer via backstepping method. Parametric uncertainties are handled by the synthesized adaptive law and the remaining uncertainties are estimated by extended state observer and then compensated in a feedforward way. Moreover, both matched uncertainties and unmatched uncertainties can be estimated by constructing an extended state observer for each channel of the considered nonlinear plant. Since parametric uncertainties can be reduced by parameter adaptation, the learning burden of extended state observer is much reduced. Consequently, high-gain feedback is avoided and improved tracking performance can be expected. The proposed controller theoretically guarantees a prescribed transient tracking performance and final tracking accuracy in general while achieving asymptotic tracking when the uncertain nonlinearities are not time-variant. The motion control of a motor-driven robot manipulator is investigated as an application example with some suitable modifications and improvements, and comparative simulation results are obtained to verify the high tracking performance nature of the proposed control strategy.

Metal–organic framework derived Co3O4/C@SiO2 yolk–shell nanoreactors with enhanced catalytic performance

Abstract: The simultaneous rational optimization of structure and composition is extremely important for improving the catalytic performance of yolk–shell nanoreactors, but has been rarely achieved so far. In this work, yolk–shell Co3O4/C@SiO2 nanoreactors (YSCCSs) are facilely synthesized by coordinating the growth of Co-zeolitic imidazole framework (ZIF-67) crystals with the hydrolysis/condensation of tetraethoxysilane (TEOS). In this process, the preliminarily formed ZIF-67 crystals play an essential role in the final structure of YSCCSs, which can be used as the template for silicon hydroxide growth and the precursors for carbon and cobalt species. To present the advantages of YSCCSs as a catalyst, sulfate-radical-based advanced oxidation processes (SR-AOPs) for bisphenol A (BPA) degradation were chosen as the model reaction. For comparison, three other catalysts, including yolk–shell Co3O4@SiO2 nanoreactors (YSCSs), and Co3O4 and ZIF-67 derived Co3O4/C nanoparticles, were also tested. The results show that YSCCSs exhibit significant BPA degradation performance over YSCSs, and CO3O4 and Co3O4/C nanoparticles. This outstanding catalytic performance is ascribed to the rational optimization of the catalyst from the following two aspects: (i) confinement effect of yolk–shell nanostructure and the improved dispersity of carbon supported Co3O4 species enhance the catalytic activity and stability (structure optimization); and (ii) the existence of graphitized carbon accelerates the electron transport from the catalyst to peroxymonosulfate (PMS) and thus increases the reaction kinetics (composition optimization). The results of this research show that metal–organic framework (MOF)-derived nanoreactors with a rational structure and composition have a promising application prospect for environmental remediation.

Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in Clouds

Abstract: Cloud computing is a suitable platform to execute the deadline-constrained scientific workflows which are typical big data applications and often require many hours to finish. Moreover, the problem of energy consumption has become one of the major concerns in clouds. In this paper, we present a cost and energy aware scheduling (CEAS) algorithm for cloud scheduler to minimize the execution cost of workflow and reduce the energy consumption while meeting the deadline constraint. The CEAS algorithm consists of five sub-algorithms. First, we use the VM selection algorithm which applies the concept of cost utility to map tasks to their optimal virtual machine (VM) types by the sub-makespan constraint. Then, two tasks merging methods are employed to reduce execution cost and energy consumption of workflow. Further, In order to reuse the idle VM instances which have been leased, the VM reuse policy is also proposed. Finally, the scheme of slack time reclamation is utilized to save energy of leased VM instances. According to the time complexity analysis, we conclude that the time complexity of each sub-algorithm is polynomial. The CEAS algorithm is evaluated using Cloudsim and four real-world scientific workflow applications, which demonstrates that it outperforms the related well-known approaches.