参数优化配置的广义奇异摄动弹性飞机模型降阶研究

推导了广义奇异摄动法的降阶方法,介绍了其在飞机模型降阶应用中的优点,针对算法中出现的参数ξ进行了分析,以弹性飞机模型为平台提出了ξ的优化配置并进行了仿真。结果表明,采用该参数配置的广义奇异摄动模型降阶方法可以用于弹性飞机模型降阶。     

MEMS扫描镜温度可靠性测试

温度是MEMS扫描镜的一个重要失效因素。研究温度对MEMS扫描镜振幅的影响,再通过温度冲击试验和高低温存储试验测试温度对MEMS扫描镜可靠性的影响,分析扫描镜在试验中是否失效和失效的原因。试验结果表明:高温对MEMS扫描镜外框架和镜面振幅无影响,低温对MEMS扫描镜外框架振幅影响较大,增幅达12.3%,对镜面振幅无影响;低温存储由于水汽凝结会导致MEMS扫描镜失效,高温存储对MEMS扫描镜可靠性无影响。     

一种能量高效的无线传感器网络拓扑控制算法

通过对现有拓扑控制算法的研究,针对无线传感器网络中节点能耗分布不均匀的问题,提出了一种能量高效的拓扑控制算法(EETCA)。该算法以均衡全局能耗为目标,综合考虑了节点的剩余能量、簇的规模、数据最优传输跳数等因素,避免了部分节点能量消耗过快,从而有效地均衡网络负载。仿真结果表明:EETCA在能耗均衡方面均优于原来的算法,延长了无线传感器网络的生命周期。     

基于协作传输的大范围WSNs能耗均衡分簇算法

由于大范围无线传感器网络（WSNs）节点的数量巨大,网络的能量消耗极不均,提出一种基于协作传输的分簇算法—EBBMCC—LS算法。该算法在保证网络均匀分簇的前提下,能保证网络中簇头节点的均匀分布,在簇间通信时加入协作传输策略,传感器节点之间通过协作传输构成虚拟多天线系统,改善系统性能,解决了大范围WSNs中的能耗不均现象。实验验证：该算法能够均衡大范围WSNs中的能耗,延长网络寿命,可促进大范围WSNs应用的推广。     

双向聚能预裂爆破切割器的研制与应用

为解决普通线性聚能切割器所用炸药等问题不能满足工程中预裂和光面爆破的要求，研究了一种双向聚能预裂爆破切割器，采用廉价的低爆速工业炸药为主装炸药，以两条高爆速炸药条带动主装炸药爆轰，使主装炸药产生聚能爆轰波，利用聚能爆轰波和聚能装药的双重聚能效果产生强烈的聚能气体或金属射流。依据这种设计思想制造出以工业炸药为主体的双向聚能预裂爆破切割器，可用于预裂、光面爆破与岩石切割等。现场试验表明，双向聚能预裂爆破切割器使用方便，爆破效果显著。     

人体局部代谢率监测系统设计

在实现将温度、湿度及辐射传感器集成于独立检测探头基础上,设计了一种人体局部代谢率监测系统。系统通过采集人体指端生理参数,根据人体能量代谢守恒法,建立数学模型并计算静息代谢率。经过实验验证:该系统可以实现代谢率的方便测量,测试结果能够反映人体正常的代谢情况。由于系统设计简便、成本低,可被广泛用于人体的能量代谢率检测。     

RECURSIVE GENERALIZED M ESTIMATES FOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. Outliers in time series seriously affect conventional parameter estimates. In this paper a robust recursive estimation procedure for the parameters of auto-regressve moving-average models with additive outliers is proposed. Using 'cleaned' residuals from an initial robust fit of an autoregression of high order as input, bounded influence regression is applied recursively. The proposal follows certain ideas of Hannan and Rissanen, who suggested a three-stage procedure for order and parameter estimation in a conventional setting.
A Monte Carlo study is performed to investigate the robustness properties of the proposed class of estimates and to compare them with various other suggestions, including least squares, M estimates, residual autocovariance and truncated residual autocovariance estimates. The results show that the recursive generalized M estimates compare favourably with them. Finally, possible modifications to master even vigourous situations are suggested.     

EXTERNAL FLUID HEATING OF A POROUS ED

This work deals with the thermal analysis of externally heated porous beds of finite length. A one dimensional model was developed that includes conduction and storage in both the fluid and bed, convective exchange between the fluid and bed, and the effect of adsorption/desorption in the bed. This model results in two coupled differential equations for the fluid and bed temperatures as functions of four independent dimensionless parameters. These equations were solved numerically using finite difference approximations. A truncation error analysis was carried out to maintain an accurate solution. The method of normalization is such that the results of this analysis are of use in bed design when the breakthrough characteristics in finite length beds are of interest. A method to measure bed thermal performance is defined and a means to optimize bed thermal performance is presented. An experiment was conducted to validate the numerically obtained results. A comparison of numerical to experimental results is presented     

无线传感器网络分簇拓扑控制算法

通过对经典的分簇算法HEED和EEUC进行研究与分析,对它们不足之处进行了改进,提出了一种新的基于双簇首节能的无线传感器网络分簇拓扑控制算法,即DCHEB算法。该算法提出了一种新的簇划分方案,通过此方案可以对无线传感器网络进行合理分簇,使得簇首节点位于合适的位置上,平均了各个簇的节点个数,可以避免簇内的边缘节点过早死亡。最后通过理论分析和仿真工具验证了该算法对减少无线传感器网络的能量消耗和延长其生存时间有很好的作用。     

Prevention from Soft Errors via Architecture Elasticity

Due to the decreasing threshold voltages, shrinking feature size, as well as the exponential growth of on-chip transistors, modern processors are increasingly vulnerable to soft errors. However, traditional mechanisms of soft error mitigation take actions to deal with soft errors only after they have been detected. Instead of the passive responses, this paper proposes a novel mechanism which proactively prevents from the occurrence of soft errors via architecture elasticity. In the light of a predictive model, we adapt the processor architectures h01istically and dynamically. The predictive model provides the ability to quickly and accurately predict the simulation target across different program execution phases on any architecture configurations by leveraging an artificial neural network model. Experimental results on SPEC CPU 2000 benchmarks show that our method inherently reduces the soft error rate by 33.2% and improves the energy efficiency by 18.3% as compared with the static configuration processor.