Delay optimization and energy balancing algorithm for improving network lifetime in fixed wireless sensor networks

Since wireless in terms of energy-restricted processes, dispersion radii, processing power limitations, buffers, bandwidth-limited connections, active network topologies, and network stream of traffic outlines, sensor networks provide difficult design issues. The number of hops and latency are decreased if there is a relay mote because it interacts directly with relay motes that are closer to the destination mote. The tremendous intensive research in the area of Wireless Sensor Networks (WSN) has gained a lot of significance among the technical community and research. The job of WSN is to sense the data using sensor motes, pass on the data to the destination detection mote which is associated with a processing center and can be used in multiple spans of Internet of Things (IoT) applications. Wireless sensor network has a set of sensor motes. By making use of sensor mote placement strategy all the sensor motes are spread in an area with each mote having its own exceptional location. Internet of things applications are delay sensitive those applications have a challenge of forming the complete path at a lower delay constraint. The proposal is to modify the game theory energy balancing algorithm by making use of relay motes so that overall network lifetime is increased. It has been proved that modified GTEB is better with respect to existing algorithms in terms of delay, figure of hops, energy depletion, figure of alive motes, figure of dead motes, lifespan ratio, routing overhead and throughput.     

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.     

基于自适应BP神经网络的结构损伤检测

描述基于人工神经网络的结构损伤检测的基本步骤以及该方法在实际5层钢框架结构损伤检测上的应用．提出了一种改进的BP神经网络方法，它能够解决传统BP神经网络在实际应用中存在的两个问题：收敛速度慢并存在局部极小．其基本思想是引入动态自适应算子加速传统BP算法的梯度下降速度，从而提高运算速度，通过自调节保证学习过程中每一时刻具有较大的sigmoid函数值，从而可以避免局部极小．数值仿真结果表明基于该自适应神经网络的结构损伤检测方法具有强的鲁棒性，而且与传统的BP神经网络相比，不仅提高了计算速度，并且具有很高的精度．最后，实例的应用也证明了该方法的有效性．     

L-leap-accelerating the stochastic simulation of chemically reacting systems

Presented here is an L-leap method for accelerating stochastic simulation of well-stirred chemically reacting systems,in which the number of reactions occurring in a reaction channel with the largest propensity function is calculated from the leap condition and the number of reactions occurring in the other reaction channels are generated by using binomial random variables during a leap.The L-leap method can better satisfy the leap condition.Numerical simulation results indicate that the L-leap method can obtain better performance than established methods.     

面向星敏感器的星模式识别算法

介绍了到目前为止出现的所有面向星敏感器的星模式识别算法,它们是概率统计算法、三角形算法、匹配组算法、网格算法、奇异值分解算法、神经网络算法和遗传算法,并分两组比较了它们的性能,给出了具有指导意义的结论。     

星载张力索网天线结构混合变量的多目标优化

以纵向拉索调整力大小和可调整的纵向拉索数目为设计变量,可调整的纵向拉索数目最少和网状反射面的均方根误差最小为目标函数,应力和频率为约束条件,建立了大型空间网状可展开天线的形面调整的多目标优化数学模型,并用遗传算法进行了优化求解。优化结果表明:该方法在提高天线表面精度的同时,能有效地减少可调整的纵向拉索数目,降低了工程人员在网面调整中的繁杂劳动。并且,网面调整的总次数也远少于实际工程中的调整,对工程应用具有一定的指导意义。     

从光闪烁频谱反演大气湍流谱:原理与数值模拟

建立了一种数值算法,从光波在湍流大气中传播的闪烁时间频谱的高频特征反演大气光学湍流谱.此算法具有两点优越性:（1）反演出的湍流谱直接表征大气折射率的起伏而非大气温度场的起伏;（2）可以获得高空间波数、大部分位于耗散区的湍流谱,而用其它方法不易获得此区域的湍流谱.对Hill湍流谱以及一种复杂的湍流谱进行了大量数值模拟来检验此种算法,结果表明:如果知道大气风速与湍流内尺度就可以相当有效地获得湍流谱.     

An efficient algorithm for hypersonic viscous flows

The CSCM-S algorithm proposed by Lombard et al. is a very attractive tool for solving multidimensional Euler and Navier-Stokes equations. However, it is not economical due to the use of global sweeps in the whole computational domain. In this paper we suggest a modified strategy, which combines a single-marching technique for supersonic dominated region with a multi-sweep procedure for pure subsonic and complex flowfield. The new algorithm may save significantly CPU time and is more suitable for engineering applications. The project supported by National Natural Science Foundation of China     

一种用于分散化滤波的融合算法及其在组合导航系统中的应用

提出了一种局部估计相关时的最优融合算法，其特点是加权矩阵是对角阵，即全局估计中的各分量分别是局部估计中相应分量的线性组合，在此融合算法中局部估计是最优的，并且不存在由主滤波器向子滤波器的信息反馈，因此具有较好的客措性。给出了最优加权矩阵的存在性和唯一性的证明，并通过与现有类似融合算法的对比，表明该算法具有计算量小的优点。最后，以组合导航系统为例作了仿真计算，结果表明该融合算法是可行的。     

基于非结构化同位网格的SIMPLE算法

通过基于非结构化网格的有限体积法对二维稳态Navier—Stokes方程进行了数值求解。其中对流项采用延迟修正的二阶格式进行离散；扩散项的离散采用二阶中心差分格式；对于压力-速度耦合利用SIMPLE算法进行处理；计算节点的布置采用同位网格技术，界面流速通过动量插值确定。本文对方腔驱动流、倾斜腔驱动流和圆柱外部绕流问题进行了计算，讨论了非结构化同位网格有限体积法在实现SIMPLE算法时，迭代次数与欠松弛系数的关系、不同网格情况的收敛性、同结构化网格的对比以及流场尾迹结构。通过和以往结果比较可知，本文的方法是准确和可信的。