The Influence of Medium on Low-Voltage Circuit Breaker Arcs

This paper mainly focuses on the influence of three kinds of media： air, air-10%PA （Nylon） and air-10% POM （polyoxymethylene） on low-voltage circuit breaker arcs. A threedimensional （3-D） model of arc motioa under the effect of external magnetic field is built based on magnetohydrodynamics （MHD） equations. By adopting the commercial computational fluid dynamics （CFD） package based on the control-volume method, the above MHD equations are solved. For the media of air-10%PA and air-10%POM, the distributions of stationary temperature and electrical potential and the transient motion processes are compared with those of air arc. The research shows that both air-10%PA and air -10% POM can cool the arc plasma and the former is more effective. Both of them can increase the stationary voltage as well. Moreover, the presence of the two mixtures can accelerate the arc motion toward the quenching area and ensures the arc quenched in time.     

丰满流域可能最大暴雨与可能最大洪水分析研究

依据丰满漉域的水文气象资料,计算丰满流域可能最大暴雨,推求白丰区间的可能最大洪水并进行成果合理性分析.     

Effect of supports on the redox performance of pyrite cinder in chemical looping combustion

Chemical looping combustion (CLC) is a clean and efficient flame-free combustion technology,which combust the fuels by lattice oxygen from a solid oxygen carrier with inherent CO2 capture.The develop-ment of oxygen carriers with low cost and high redox performance is crucial to the whole efficiency of CLC process.As the solid by-product from the sulfuric acid production,pyrite cinder presented excellent redox performance as an oxygen carrier in CLC process.The main components in pyrite cinder are Fe2O3,CaSO4,Al2O3 and SiO2 in which Fe2O3 is the active component to provide lattice oxygen.In order to sys-tematic investigate the functions of supports (CaSO4,Al2O3 and SiO2) in pyrite cinder,three oxygen car-riers (Fe2O3-CaSO4,Fe2O3-Al2O3 and Fe2O3-SiO2) were prepared and evaluated in this study.The results showed that Fe2O3-CaSO4 displayed high redox activity and cycling stability in the multiple redox cycles.However,both Fe2O3-Al2O3 and Fe2O3-SiO2 experienced serious deactivation during redox reactions.It indicated that the inert Fe-Si solid solution (Fe2SiO4) was formed in the spent Fe2O3-SiO2 sample,which decreased the oxygen carrying capacity of this sample.The XPS results showed that the oxygen species on the surface of Fe2O3-CaSO4 could be fully recovered after the 20 redox cycles.It can be concluded that CaSO4 is the key to the high redox activity and cycling stability of pyrite cinder.     

35kV变压器故障气相色谱分析

对于运行中的变压器,气相色谱分析法可实现其内部故障的诊断,并提供有效的运维建议.应用气相色谱分析法分析一起35 kV变压器故障,为气相色谱分析法的进一步推广应用提供有力的技术支撑.     

Selective hydrogenolysis of glycerol to propylene glycol on Cu–ZnO catalysts

Hydrogenolysis of biomass-derived glycerol is an alternative route to sustainable production of propylene glycol. Cu–ZnO catalysts were prepared by coprecipitation with a range of Cu/Zn atomic ratio (0.6–2.0) and examined in glycerol hydrogenolysis to propylene glycol at 453–513 K and 4.2 MPa H₂. These catalysts possess acid and hydrogenation sites required for bifunctional glycerol reaction pathways, most likely involving glycerol dehydration to acetol and glycidol intermediates on acidic ZnO surfaces, and their subsequent hydrogenation on Cu surfaces. Glycerol hydrogenolysis conversions and selectivities depend on Cu and ZnO particle sizes. Smaller ZnO and Cu domains led to higher conversions and propylene glycol selectivities, respectively. A high propylene glycol selectivity (83.6%), with a 94.3% combined selectivity to propylene glycol and ethylene glycol (also a valuable product) was achieved at 22.5% glycerol conversion at 473 K on Cu–ZnO (Cu/Zn = 1.0) with relatively small Cu particles. Reaction temperature effects showed that optimal temperatures (e.g. 493 K) are required for high propylene glycol selectivities, probably as a result of optimized adsorption and transformation of the reaction intermediates on the catalyst surfaces. These preliminary results provide guidance for the synthesis of more efficient Cu–ZnO catalysts and for the optimization of reaction parameters for selective glycerol hydrogenolysis to produce propylene glycol.     

Augmented Online Sequential Quaternion Extreme Learning Machine

Neural Processing Letters - Online sequential extreme learning machine (OS-ELM) is one of the most popular real-time learning strategy for feedforward neural networks with single hidden layer due...     

Electrochemical investigation of La0.4Sr0.6TiO3 synthesized in air for SOFC application

Journal of Applied Electrochemistry - La-doped titanate materials have been widely investigated as alternative Ni-free anodes for solid oxide fuel cells (SOFCs). In this study, La0.4Sr0.6TiO3 (LST)...     

Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function

Bartels–Stewart algorithm is an effective and widely used method with an O(n ³) time complexity for solving a static Sylvester equation. When applied to time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. Gradient-based recurrent neural network are able to solve the time-varying Sylvester equation in real time but there always exists an estimation error. In contrast, the recently proposed Zhang neural network has been proven to converge to the solution of the Sylvester equation ideally when time goes to infinity. However, this neural network with the suggested activation functions never converges to the desired value in finite time, which may limit its applications in realtime processing. To tackle this problem, a sign-bi-power activation function is proposed in this paper to accelerate Zhang neural network to finite-time convergence. The global convergence and finite-time convergence property are proven in theory. The upper bound of the convergence time is derived analytically. Simulations are performed to evaluate the performance of the neural network with the proposed activation function. In addition, the proposed strategy is applied to online calculating the pseudo-inverse of a matrix and nonlinear control of an inverted pendulum system. Both theoretical analysis and numerical simulations validate the effectiveness of proposed activation function.