Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

Thermally Driven Structure and Performance Evolution of Atomically Dispersed FeN4 Sites for Oxygen Reduction

Abstract: FeN4 moieties embedded in partially graphitized carbon are the most efficient platinum group metal free active sites for the oxygen reduction reaction in acidic proton-exchange membrane fuel cells. However, their formation mechanisms have remained elusive for decades because the Fe-N bond formation process always convolutes with uncontrolled carbonization and nitrogen doping during high-temperature treatment. Here, we elucidate the FeN4 site formation mechanisms through hosting Fe ions into a nitrogen-doped carbon followed by a controlled thermal activation. Among the studied hosts, the ZIF-8-derived nitrogen-doped carbon is an ideal model with well-defined nitrogen doping and porosity. This approach is able to deconvolute Fe-N bond formation from complex carbonization and nitrogen doping, which correlates Fe-N bond properties with the activity and stability of FeN4 sites as a function of the thermal activation temperature.

Binary dragonfly optimization for feature selection using time-varying transfer functions

Abstract: The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that was shown to have excellent performance for numerous optimization problems. In this paper, a wrapper-feature selection algorithm is proposed based on the Binary Dragonfly Algorithm (BDA). The key component of the BDA is the transfer function that maps a continuous search space to a discrete search space. In this study, eight transfer functions, categorized into two families (S-shaped and V-shaped functions) are integrated into the BDA and evaluated using eighteen benchmark datasets obtained from the UCI data repository. The main contribution of this paper is the proposal of time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation. During the early stages of the optimization process, the probability of changing the position of an element is high, which facilitates the exploration of new solutions starting from the initial population. On the other hand, the probability of changing the position of an element becomes lower towards the end of the optimization process. This behavior is obtained by considering the current iteration number as a parameter of transfer functions. The performance of the proposed approaches is compared with that of other state-of-art approaches including the DA, binary grey wolf optimizer (bGWO), binary gravitational search algorithm (BGSA), binary bat algorithm (BBA), particle swarm optimization (PSO), and genetic algorithm in terms of classification accuracy, sensitivity, specificity, area under the curve, and number of selected attributes. Results show that the time-varying S-shaped BDA approach outperforms compared approaches.