雷达组网系统误差校正的一种方法

建立组网的系统误差模型，用非线性规划方法求系统误差。利用加入系统误差的模拟目标，验证优化算法的有效性；应用该算法对三站实际数据提取系统误差。

用于电力塔远程监控的无线传感器网络装置

设计一种了解电力塔实时情况的远程监控装置,通信时运用无线传感器网络技术并结合ZigBee中的算法协议进行分析以求达到节能高效并最终延长设备使用生命周期的目标。结合电力塔分布的实际情况在通信过程中运用了一种新型的通信路由算法,即线性通信组网方式;信息传输时考虑到实际的节能要求,采用了分布式功率控制的方法。

Scalable and Identifier/Locator-Splitting Routing Protocol for Mobile Ad Hoc Networks

In the traditional Intemet Protocol （IP） architecture, there is an overload of IP sermntic problems. Existing solutions focused mainly on the infrastructure for the fixed network, and there is a lack of support for Mobile Ad Hoc Networks （MANETs）. To improve scalability, a routing protocol for MANETs is presented based on a locator named Tree-structure Locator Distance Vector （TLDV）. The hard core of this routing method is the identifier/locator split by the Distributed Hash Table （DHT） method, which provides a scalable routing service. The node locator indicates its relative location in the network and should be updated whenever topology changes, kocator space ks organized as a tree-structure, and the basic routing operation of the TLDV protocol is presented. TLDV protocol is compared to some classical routing protocols for MANETs on the NS2 platform Results show that TLDV has better scalability. Key words：

Relay movement control for maintaining connectivity in aeronautical ad hoc networks

As a new sort of mobile ad hoc network(MANET), aeronautical ad hoc network(AANET) has fleet-moving airborne nodes(ANs) and suffers from frequent network partitioning due to the rapid-changing topology. In this work, the additional relay nodes(RNs) is employed to repair the network and maintain connectivity in AANET. As ANs move, RNs need to move as well in order to re-establish the topology as quickly as possible. The network model and problem definition are firstly given, and then an online approach for RNs' movement control is presented to make ANs achieve certain connectivity requirement during run time. By defining the minimum cost feasible moving matrix(MCFM), a fast algorithm is proposed for RNs' movement control problem. Simulations demonstrate that the proposed algorithm outperforms other control approaches in the highly-dynamic environment and is of great potential to be applied in AANET.

Power Big Data Fusion Prediction

This paper is a research on the characteristics of power big data. According to the characteristics of ＂large volume＂, ＂species diversity＂, ＂sparse value density＂, ＂fast speed＂ of the power big data, a prediction model of multi-source information fusion for large data is established, the fusion prediction of various parameters of the same object is realized. A combined algorithm of Map Reduce and neural network is used in this paper. Using clustering and nonlinear mapping ability of neural network, it can effectively solve the problem of nonlinear objective function approximation, and neural network is applied to the prediction of fusion. In this paper, neural network model using multi layer feed forward network--BP neural network. Simultaneously, to achieve large-scale data sets in parallel computing, the parallelism and real-time property of the algorithm should be considered, further combined with Reduce Map model, to realize the parallel processing of the algorithm, making it more suitable for the study of the fusion of large data. And finally, through simulation, it verifies the feasibility of the proposed model and algorithm.

A Complex Systems Science Perspective on Wireless Networks

The paper targets a future world where all wireless networks are self-organising entities and in which the predominant mode of spectrum access is dynamic. The paper explores whether the behaviour of a collection of autonomous self-organising wireless systems can be treated as a complex system and whether complex systems science can shed light on the design and deployment of these networks. The authors focus on networks that self-organise from a frequency perspective to understand the behaviour of a collection of wireless self-organising nodes. Each autonomous network is modelled as a cell in a lattice and follows a simple set of self-organisation rules. Two scenarios are considered, one in which each cell is based on cellular automata and which provides an abstracted view of interference and a second in which each cell uses a self-organising technique which more accurately accounts for interference. The authors use excess entropy to measure complexity and in combination with entropy gain an understanding of the structure emerging in the lattice for the self-organising networks. The authors show that the self-organising systems presented here do exhibit complex behaviour. Finally,the authors look at the robustness of these complex systems and show that they are robust against changes in the environment.

Modeling of daily pan evaporation using partial least squares regression

This study presented the application of partial least squares regression （PLSR） in estimating daily pan evaporation by utilizing the unique feature of PLSR in eliminating collinearity issues in predictor variables. The climate variables and daily pan evaporation data measured at two weather stations located near Elephant Butte Reservoir, New Mexico, USA and a weather station located in Shanshan County, Xinjiang, China were used in the study. The nonlinear relationship between climate variables and daily pan evaporation was successfully modeled using PLSR approach by solving collinearity that exists in the climate variables. The modeling results were compared to artificial neural networks （ANN） models with the same input variables. The resuits showed that the nonlinear equations developed using PLSR has similar performance with complex ANN approach for the study sites. The modeling process was straightforward and the equations were simpler and more explicit than the ANN black-box models.