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.

One-step synthesis of copper compounds on copper foil and their supercapacitive performance

Abstract: Nanowire-like Cu(OH)2 arrays, microflower-like CuO standing on Cu(OH)2 nanowires and hierarchical CuO microflowers are directly synthesized via a simple and cost-effective liquid–solid reaction. The specific capacitance of Cu(OH)2, CuO/Cu(OH)2 and CuO are 511.5, 78.44 and 30.36 F g−1, respectively, at a current density of 5 mA cm−2. Therefore, the Cu(OH)2/Cu-foil electrode displays the best supercapacitive performance. The capacitance retention reaches up to 83% after 5000 charge/discharge cycles with the columbic efficiency of ∼98%. More importantly, the nanowire Cu(OH)2 transformed into stable nanosheet CuO after about 600 constant current charge–discharge cycles. Additionally, we fabricate an asymmetric supercapacitor with nanowire Cu(OH)2/Cu-foil as a positive electrode, activated carbon (AC) as a negative electrode and 6 mol dm−3 KOH as electrolyte, which exhibits an energy density of 18.3 W h kg−1 at a power density of 326 W kg−1.

The synthesis of a manganese dioxide–iron oxide–graphene magnetic nanocomposite for enhanced uranium(VI) removal

Abstract: In this study, we have developed a facile route for the fabrication of a manganese dioxide–iron oxide–reduced graphite oxide magnetic nanocomposite (MnO2–Fe3O4–rGO). The as-obtained nanomaterial (MnO2–Fe3O4–rGO) was characterized using transmission electron microscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, vibrating sample magnetometry, and Brunauer–Emmett–Teller surface area measurements. The MnO2–Fe3O4–rGO composite shows extraordinary adsorption capacity and fast adsorption rates for the removal of uranium(VI) in aqueous solution. The influence of factors including the dosage of the MnO2–Fe3O4–rGO composite used, pH of aqueous solution, and temperature were investigated. The thermodynamic parameters, including Gibbs free energy (ΔG°), standard enthalpy change (ΔH°) and standard entropy change (ΔS°) for the process, were calculated using the Langmuir constants. The results show that a pseudo-second-order kinetics model can be used to describe the uptake process using a kinetics test. Our present study suggests that the MnO2–Fe3O4–rGO composite can be used as a potential adsorbent for sorption of uranium(VI) as well as for providing a simple, fast separation method for the removal of uranium(VI) ions from aqueous solution.

A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy.

Abstract: The ongoing Coronavirus Disease 2019 (COVID-19) pandemic threatens the health of humans and causes great economic losses. Predictive modeling and forecasting the epidemic trends are essential for developing countermeasures to mitigate this pandemic. We develop a network model, where each node represents an individual and the edges represent contacts between individuals where the infection can spread. The individuals are classified based on the number of contacts they have each day (their node degrees) and their infection status. The transmission network model was respectively fitted to the reported data for the COVID-19 epidemic in Wuhan (China), Toronto (Canada), and the Italian Republic using a Markov Chain Monte Carlo (MCMC) optimization algorithm. Our model fits all three regions well with narrow confidence intervals and could be adapted to simulate other megacities or regions. The model projections on the role of containment strategies can help inform public health authorities to plan control measures.

LPI Radar Waveform Recognition Based on Time-Frequency Distribution.

Abstract: In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB.