无线传感网中一种智能数据融合算法的实现及仿真分析

在无线传感网中,传感器节点一般都由自身装配的电池供电,难以进行电量补充,因此节约电量对于无线传感网来说至关重要.为了提高无线传感网能量使用效率,延长网络生存时间,提出了一种结合遗传算法和粒子群算法优化BP神经网络的智能数据融合算法 GAPSOBP(BP Neural Network Data Fusion algorithm optimized by Genetic algorithm and Particle swarm).GAPSOBP算法将无线传感网的节点类比为BP神经网络中的神经元,通过神经网络提取无线传感网采集的感知数据并结合分簇路由对收集的传感数据进行融合处理,从而大幅减少发往汇聚节点的网络数据量.仿真结果表明,与经典LEACH算法和PSOBP算法相比,GAPSOBP算法能有效减少网络通信量,节约节点能量,显著延长网络生存时间.     

基于RLS和EKF算法的全钒液流电池SOC估计

针对全钒液流电池的荷电状态(SOC)估计精度低、估计成本较高等问题,提出一种基于递推最小二乘算法(RLS)与扩展卡尔曼滤波算法(EKF)相结合的估计方法.该方法通过RLS算法辨识所建立的钒电池数学模型参数,通过EKF算法估计钒电池的SOC,将二者结合实现电池参数发生变化时准确估计钒电池的SOC.以5kW/ 30kWh的钒电池为对象,应用所提出的算法实现钒电池的SOC估计.结果表明,该算法可以准确估计钒电池的SOC,且可节省额外增加单片检测电池测量SOC的费用.     

WSGD-OSC Scheme for Two-Dimensional Distributed Order Fractional Reaction–Diffusion Equation

In this paper, a new numerical approximation is discussed for the two-dimensional distributed-order time fractional reaction–diffusion equation. Combining with the idea of weighted and shifted Grünwald difference (WSGD) approximation (Tian et al. in Math Comput 84:1703–1727, 2015; Wang and Vong in J Comput Phys 277:1–15, 2014) in time, we establish orthogonal spline collocation (OSC) method in space. A detailed analysis shows that the proposed scheme is unconditionally stable and convergent with the convergence order \(\mathscr {O}(\tau ^2+\Delta \alpha ^2+h^{r+1})\), where \(\tau , \Delta \alpha , h\) and r are, respectively the time step size, step size in distributed-order variable, space step size, and polynomial degree of space. Interestingly, we prove that the proposed WSGD-OSC scheme converges with the second-order in time, where OSC schemes proposed previously (Fairweather et al. in J Sci Comput 65:1217–1239, 2015; Yang et al. in J Comput Phys 256:824–837, 2014) can at most achieve temporal accuracy of order which depends on the order of fractional derivatives in the equations and is usually less than two. Some numerical results are also given to confirm our theoretical prediction.     

Orthogonal spline collocation method for the fourth-order diffusion system

The fourth-order diffusion systems depict the wave and photon propagation in intense laser beams and play an important role in the phase separation in binary mixture. In this paper, by using orthogonal spline collocation (OSC) method in spatial direction and classical L1 approximation in temporal direction, a fully discrete scheme is established for a class of fourth-order fractional reaction–diffusion equations. For the original unknown $u$ and auxiliary variable $v=\Delta u$, the full-discrete unconditional stabilities based on a priori analysis are derived by virtue of properties of OSC. Moreover, the convergence rates in $L^2$-norm for unknown $u$ are strictly investigated. At the same time, the optimal error estimates in $H^1$-norm for unknown $u$ and in $L^2$-norm for variable $v$, are also derived, respectively. For further verifying the theoretical analysis, some numerical examples are provided.     

Encoding Information on the Excited State of a Molecular Spin Chain

The quantum states of nano-objects can drive electrical transport properties across lateral and local-probe junctions. This raises the prospect, in a solid-state device, of electrically encoding information at the quantum level using spin-flip excitations between electron spins. However, this electronic state has no defined magnetic orientation and is short-lived. Using a novel vertical nanojunction process, these limitations are overcome and this steady-state capability is experimentally demonstrated in solid-state spintronic devices. The excited quantum state of a spin chain formed by Co phthalocyanine molecules coupled to a ferromagnetic electrode constitutes a distinct magnetic unit endowed with a coercive field. This generates a specific steady-state magnetoresistance trace that is tied to the spin-flip conductance channel, and is opposite in sign to the ground state magnetoresistance term, as expected from spin excitation transition rules. The experimental 5.9 meV thermal energy barrier between the ground and excited spin states is confirmed by density functional theory, in line with macrospin phenomenological modeling of magnetotransport results. This low-voltage control over a spin chain's quantum state and spintronic contribution lay a path for transmitting spin wave-encoded information across molecular layers in devices. It should also stimulate quantum prospects for the antiferromagnetic spintronics and oxides electronics communities.     

Multi-Fidelity High-Throughput Optimization of Electrical Conductivity in P3HT-CNT Composites

Combining high-throughput experiments with machine learning accelerates materials and process optimization toward user-specified target properties. In this study, a rapid machine learning-driven automated flow mixing setup with a high-throughput drop-casting system is introduced for thin film preparation, followed by fast characterization of proxy optical and target electrical properties that completes one cycle of learning with 160 unique samples in a single day, a > 10 ×  improvement relative to quantified, manual-controlled baseline. Regio-regular poly-3-hexylthiophene is combined with various types of carbon nanotubes, to identify the optimum composition and synthesis conditions to realize electrical conductivities as high as state-of-the-art 1000 S cm⁻¹. The results are subsequently verified and explained using offline high-fidelity experiments. Graph-based model selection strategies with classical regression that optimize among multi-fidelity noisy input-output measurements are introduced. These strategies present a robust machine-learning driven high-throughput experimental scheme that can be effectively applied to understand, optimize, and design new materials and composites.     

认知干扰决策关键技术研究综述

电子战是当代信息化战争的开端,战场多时空、多维域的信息掌控权决定着战争的胜负.信息化战场的决策问题不同于传统作战指挥问题,由于战场环境的复杂性、信息的多样性、装备的信息化,干扰决策问题的研究愈加重要.目前,人工智能领域的快速发展,认知无线电、认知电子战的概念相继被提出.在基于现有研究成果的基础上,介绍了认知干扰决策理论,并梳理了国内外的相关文献,主要从认知干扰决策涉及的3个方面(状态识别预测、参量控制、资源调度)出发,归纳了常用的理论方法及其存在的不足,最后对未来雷达认知干扰决策的前景予以展望.     

Electrochemical deprotonation of phosphate on stainless steel

Voltammetric experiments performed in phosphate buffer at constant pH 8.0 on platinum and stainless steel revealed clear reduction currents, which were correlated to the concentrations of phosphate. On the basis of the reactions proposed previously, a model was elaborated, assuming that both H₂PO₄⁻ and HPO₄²⁻ underwent cathodic deprotonation, and including the acid-base equilibriums. A kinetic model was derived by analogy with the equations generally used for hydrogen evolution. Numerical fitting of the experimental data confirmed that the phosphate species may act as an efficient catalyst of hydrogen evolution via electrochemical deprotonation. This reaction may introduce an unexpected reversible pathway of hydrogen formation in the mechanisms of anaerobic corrosion. The possible new insights offered by the electrochemical deprotonation of phosphate in microbially influenced corrosion was finally discussed.     

Catalytic conversion ofHevea brasiliensis andVirola sebifera oils to hydrocarbon fuels

Catalytic hydrocracking ofHevea brasiliensis (Seringa) andVirola sebifera (Ucuuba) oils was carried out in the presence of a NiMo/Al₂O₃ catalyst sulfidizedin situ with elemental sulfur at 360°C. The initial pressure of hydrogen was 140 bars. Conversions of nearly 100% of the vegetable oils were achieved. The compositions of the liquid fuels were studied in detail by gas chromatography/mass spectrometry. The main products were found to be alkanes, cycloalkanes, aromatics and carboxylic acids.