Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

Novel core-shell-like Ni-supported hierarchical beta zeolite catalysts on bioethanol steam reforming

Hierarchical-Beta zeolites have been hydrothermally synthesized by adding a new gemini organic surfactant. The used gemini surfactant play the role of a “pore-forming agents” on the mesoscale, on the same time, providing alkaline environment for the system. With this hierarchical Beta zeolite as the core support, we successfully prepared a shell layer of Ni-containing (22 wt%) petal-like core-shell-like catalyst and applied it to bioethanol steam reforming. At the reaction temperature of 350 °C–550 °C, the conversion rate of ethanol and the selectivity of hydrogen were always above 85% and 70%. After reaction of 100 h on stream at 400 °C, there were not obvious inactivation could be observed on NiNPs/OH-MBeta catalyst.     

异构分层无线网络中基于逗留时间的动态流量均衡算法研究

为使异构分层无线网络能服务更多的移动用户,提出了一种基于逗留时间的动态流量均衡算法.该算法首先根据用户移动模型计算其在小区内的逗留时间,然后基于小区呼叫到达率和重叠覆盖小区的流量状态来确定一个周期内呼叫转移的数量,最后依据逗留时间门限值将重负载小区中满足条件的呼叫转移到轻负载的重叠覆盖小区中.为降低切换呼叫掉线率,还对异构网间的呼叫切换策略做了改进.仿真实验结果表明,本算法在新呼叫阻塞率和切换呼叫掉线率等性能指标上比传统方法有显著的提高.     

介孔-大孔结构无定形硅-铝催化剂的核磁共振研究

合成了具有介孔-大孔双模型分级结构的无定形硅-铝催化剂,利用硅和铝核的固体魔角核磁共振谱,考察了合成样品中硅和铝结构上的原子特征.     

Cd-ZSM-5沸石催化剂的制备、表征和芳构化催化性能

采用离子交换、浸渍和混合三种方法制备镉改性ZSM-5沸石催化剂,考察了改性方法和镉含量对催化剂的表面酸性和Cd状态以及芳构化性能的影响。结果表明,加入镉降低了B酸性,Cd~(2+)与沸石结合形成了L_(1612)酸中心,其生成量取决于改性方法和Cd含量,并与芳构化活性提高有直接关系。     

NaY晶化母液制备硅铝胶的技术探讨

在导向剂法合成Y型分子筛的工艺下，晶化母液中SiO₂的含量为50～55g/l,Na₂O含量为20～25g/l，相当于模数为1.65～2.58的稀水玻璃，将晶化母液分离处理后，在晶化母液中加入一定量的硫酸铝溶液使硅沉淀,经过滤和水洗后即可制备出合格的硅铝胶，以便进行重新利用。