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 & Microstructure. The organization has 31149 authors who have published 27940 publications receiving 276787 citations. The organization is also known as: HEU.

Nematodes as indicators of soil recovery in tailings of a lead/zinc mine

Abstract: Trophic groups and functional guilds of soil nematodes were measured under four mine tailing subsystems in the Baoshan lead/zinc mine, Hunan Province, southern China to test the indicator value of nematodes for heavy metal pollution. No obvious correlation was found between heavy metal concen- tration and the total number of nematodes. However, the densities of c-p3, c-p4 and c-p5 nematodes were negatively correlated with Pb and Zn concentrations, suggesting that the abundance of nematode groups of high c-p values is useful indicators of heavy metal contamination. The ''weighted faunal analysis'' provided a better assessment of soil health condition than Maturity Index (MI) in situations where there were extremely low numbers of soil nematodes. Results showed that the effect of heavy metal contamination on soil nematodes might be strongly influenced by plants. Although the abundance of plant-feeding nematodes did not reflect the heavy metal conditions in the soil, it might be used as an index for assessing the soil remediation potential of pioneering plants. Patrinia villosa seems superior to Viola baoshanensis as a pioneer plant species for soil remediation based on analysis of rhizosphere nematode community.

Adversarial Attacks in Modulation Recognition With Convolutional Neural Networks

Abstract: Deep learning (DL) models are vulnerable to adversarial attacks, by adding a subtle perturbation which is imperceptible to the human eye, a convolutional neural network (CNN) can lead to erroneous results, which greatly reduces the reliability and security of the DL tasks. Considering the wide application of modulation recognition in the communication field and the rapid development of DL, by adding a well-designed adversarial perturbation to the input signal, this article explores the performance of attack methods on modulation recognition, measures the effectiveness of adversarial attacks on signals, and provides the empirical evaluation of the reliabilities of CNNs. The results indicate that the accuracy of the target model reduce significantly by adversarial attacks, when the perturbation factor is 0.001, the accuracy of the model could drop by about 50 ${\%}$ on average. Among them, iterative methods show greater attack performances than that of one-step method. In addition, the consistency of the waveform before and after the perturbation is examined, to consider whether the added adversarial examples are small enough (i.e., hard to distinguish by human eyes). This article also aims at inspiring researchers to further promote the CNNs reliabilities against adversarial attacks.

N-Doped graphene-supported Co@CoO core–shell nanoparticles as high-performance bifunctional electrocatalysts for overall water splitting

Abstract: The alkaline electrolyzer fabricated by employing Co@CoO core–shell nanoparticles on N-doped graphene as bifunctional catalysts exhibits remarkable activity with a current density of 10 mA cm−2 at a cell voltage of 1.58 V and considerable stability over 8 h of continuous electrolysis operation at 100 mA cm−2, favorably comparable to the integrated performance of Pt and IrO2.