Southeast University

About: Southeast University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Computer science & MIMO. The organization has 66363 authors who have published 79434 publications receiving 1170576 citations. The organization is also known as: SEU.

Growth mechanism of tubular ZnO formed in aqueous solution.

Abstract: Tubular ZnO microstructural arrays were fabricated by a hydrothermal decomposition method. The dependence of the morphologies on the growth time and temperature was investigated in detail. An experiment was carried out to determine the mechanism of tubular ZnO formation. Our results showed that ZnO microtubes originated from an ageing process from ZnO microrods at a lower temperature (compared to the temperature when hydrothermal deposition of ZnO microrods was dominant) due to the preferential chemical dissolution of the metastable Zn-rich (0001) polar surfaces. A growth model was proposed based on the coexistence of hydrothermal deposition and dissolution of ZnO in the fabrication process.

Installing earth-abundant metal active centers to covalent organic frameworks for efficient heterogeneous photocatalytic CO2 reduction

Abstract: Photocatalytic conversion of CO2 into energy carriers has been recognized as a highly promising strategy for achieving the virtuous carbon cycle in nature. The realization of this process depends on an efficient catalyst to reduce the reaction barrier. Herein, we report a series of transition metal ion modified crystalline covalent organic frameworks (COFs) for the heterogeneous photocatalytic reduction of CO2. By coordinating different kinds of open metal active species into COFs, the resultant DQTP (2,6-diaminoanthraquinone - 2,4,6-triformylphloroglucinol) COF-M (M = Co/Ni/Zn) exerts a strong influence on the activity and selectivity of products (CO or HCOOH). Significantly, DQTP COF-Co exhibits a high CO production rate of 1.02 × 103 μmol h−1 g−1, while DQTP COF-Zn has a high selectivity (90% over CO) for formic acid generation (152.5 μmol h−1 g−1). This work highlights the great potential of using stable COFs as platforms to anchor earth-abundant metal active sites for heterogeneous CO2 reduction.

Soft computing based on maximizing consensus and fuzzy TOPSIS approach to interval-valued intuitionistic fuzzy group decision making

Abstract: We define the consensus index from the perspective of the ranking of decision information and derive the experts' weights on the basis of the idea of the maximizing consensus.We extend the TOPSIS approach to rank all the alternatives from the perspective of the magnitude of decision information under interval-valued intuitionistic fuzzy environment.Combining with the multi-choice goal programming models, our approach can not only find the optimal alternative(s) but also determine their corresponding optimum quantities. Multi-attribute group decision making (MAGDM) is an important research topic in decision theory. In recent decades, many useful methods have been proposed to solve various MAGDM problems, but very few methods simultaneously take them into account from the perspectives of both the ranking and the magnitude of decision data, especially for the interval-valued intuitionistic fuzzy decision data. The purpose of this paper is to develop a soft computing technique based on maximizing consensus and fuzzy TOPSIS in order to solve interval-valued intuitionistic fuzzy MAGDM problems from such two aspects of decision data. To this end, we first define a consensus index from the perspective of the ranking of decision data, for measuring the degree of consensus between the individual and the group. Then, we establish an optimal model based on maximizing consensus to determine the weights of experts. Following the idea of TOPSIS, we calculate the closeness indices of the alternatives from the perspective of the magnitude of decision data. To identify the optimal alternatives and determine their optimum quantities, we further construct a multi-choice goal programming model based on the derived closeness indices. Finally, an example is given to verify the developed method and to make a comparative analysis.

Network-Based Quantized Control for Fuzzy Singularly Perturbed Semi-Markov Jump Systems and its Application

Abstract: This paper deals with the quantized control problem for nonlinear semi-Markov jump systems subject to singular perturbation under a network-based framework. The nonlinearity of the system is well solved by applying Takagi–Sugeno (T-S) fuzzy theory. The semi-Markov jump process with the memory matrix of transition probability is introduced, for which the obtained results are more reasonable and less limiting. In addition, the packet dropouts governed by a Bernoulli variable and the signal quantization associated with a logarithmic quantizer are deeply studied. The major goal is to devise a fuzzy controller, which not only assures the mean-square $\bar { \sigma }$ -error stability of the corresponding system but also allows a higher upper bound of the singularly perturbed parameter. Sufficient conditions are developed to make sure that the applicable controller could be found. The further examination to demonstrate the feasibility of the presented method is given by designing a controller of a series DC motor model.