Harbin Engineering University

About: Harbin Engineering University is a education organization based out in Harbin, Heilongjiang, China. It is known for research contribution in the topics: Control theory & Computer science. The organization has 31149 authors who have published 27940 publications receiving 276787 citations. The organization is also known as: HEU.

Bimetallic Ni–Mo nitride nanotubes as highly active and stable bifunctional electrocatalysts for full water splitting

Abstract: Designing low-cost, highly active and stable electrocatalysts is very important to various renewable energy storage and conversion devices Herein we develop a facile method to fabricate bimetallic Ni–Mo nitride nanotubes, which can serve as highly active and stable bifunctional electrocatalysts for full water splitting To drive a current density of 10 mA cm−2, the bimetallic Ni–Mo nitride nanotubes require an overpotential of 295 mV for the OER and 89 mV for the HER The alkaline water electrolyzer with the nanotubes as cathode and anode catalysts requires a cell voltage of ca 1596 V to achieve a current density of 10 mA cm−2 Furthermore, the nanotubes for full water splitting show excellent stability even at a high current density of 370 mA cm−2, superior to the integrated performance of commercial Pt and IrO2 Our experimental results show that the NiOOH and NH groups formed at the catalyst surface during the OER process are active species for the OER, while the Ni(OH)2, NH and Mo species at the catalyst surface play a key role in the HER The present strategy may open an avenue for fabrication of low-cost, highly active and stable electrocatalysts for large-scale water splitting

The enhanced ethanol sensing properties of multi-walled carbon nanotubes/SnO2 core/shell nanostructures

Abstract: Multi-walled carbon nanotubes/SnO2 (CNT/SnO2) core/shell nanostructures were synthesized by a simple wet-chemical method The thickness of the SnO2 shell was about 10 nm and the diameters of the SnO2 particles were 2–8 nm Sensors based on the core/shell heterostructures exhibited enhanced ethanol sensing properties The sensitivity to 50 ppm ethanol was up to 245, and the response time and recovery time were about 1 and 10 s, respectively In addition, the fluctuation of the sensitivity was less than ± 3% on remeasurement after 3 months These results indicate that the core/shell nanostructures are potentially new sensing materials for fabricating gas sensors

Convolutional Neural Networks for Automatic Cognitive Radio Waveform Recognition

Abstract: Cognitive radio technology is an important branch in the field of wireless communication, and automatic identification is a major part of cognitive radio technology. Convolutional neural network (CNN) is an advanced neural network, which is the forefront of application in the digital image recognition area. In this paper, we explore CNN in an automatic system to recognize the cognitive radio waveforms. Excitedly, it is a more effective model with high ratio of successful recognition (RSR) under high power background noise. The system can identify eight kinds of signals, including binary phase shift keying (Barker codes modulation) linear frequency modulation, Costas codes, Frank code, and polytime codes (T1, T2, T3, and T4). The recognition part includes a CNN classifier. First, we determine the appropriate architecture to make CNN effective for proposed system. Specifically, we focus on how many convolutional layers are needed, what appropriate number of hidden units is, and what the best pooling strategy is. Second, we research how to obtain the image features into CNN that based on Choi–Williams time-frequency distribution. Finally, by means of the simulations, the results of classification are demonstrated. Simulation results show the overall RSR is 93.7% when the signal-to-noise ratio is −2dB.