梯度法求解弹道导弹扰动引力的并行方法研究

根据远程弹道导弹在发射过程中对外部空间扰动引力场信息的需求,运用梯度法对外部空间扰动引力进行研究,为克服远程弹道导弹扰动引力计算模型复杂、计算量大、不容易在弹上计算机上实时计算的缺点,利用3台计算机组成主从模式并行计算模型,快速解算外部空间扰动引力。实验结果表明,该并行方法可以满足弹载计算机快速计算的要求,并能达到较高的精度。     

一种基于剩余能量和负载的AODV路由协议

移动自组网中的按需距离矢量路由协议(AODV)是基于最小跳数的,没有考虑能量消耗均衡及网络生命周期的问题,造成某些节点过早死亡。针对这一问题,提出了一种基于剩余能量和节点缓存队列负载的路由协议,有效地避免了能量较低和负载较重的节点参与路由。仿真实验结果表明,所提出的新协议有效地减少了端对端平均延时,延长了网络生命周期。     

A Parallel Interval Computation Model for Global Optimization with Automatic Load Balancing

In this paper,we propose a decentralized parallel computation model for global optimization using interval analysis.The model is adaptive to any number of processors and the workload is automatically and evenly distributed among all processors by alternative message passing.The problems received by each processor are processed based on their local dominance properties,which avoids unnecessary interval evaluations.Further,the problem is treated as a whole at the beginning of computation so that no initial decomposition scheme is required.Numerical experiments indicate that the model works well and is stable with different number of parallel processors,distributes the load evenly among the processors,and provides an impressive speedup,especially when the problem is time-consuming to solve.     

Active Store Window: Enabling Far Store-Load Forwarding with Scalability and Complexity-Efficiency

Conventional dynamically scheduled processors often use fully associative structures named load/store queue (LSQ) to implement the value communication between loads and the older in-flight stores and to detect the store-load order violation.But this in-flight forwarding only occupies about 15% of all store-load communications,which makes the CAM-based micro-architecture the major bottleneck to scale store-load communication further.This paper presents a new micro-architecture named ASW (short for active store window).It provides a new structure named speculative active store window to implement more aggressively speculative store-load forwarding than conventional LSQ.This structure could forward the data of committed stores to the executing loads without accessing to L1 data cache,which is referred to as far forwarding in this paper.At the back-end of the pipeline,it uses in-order load re-execution filtered by the tagged SSBF (short for store sequence bloom filter) to verify the correctness of the store-load forwarding.The speculative active store window and tagged store sequence bloom filter are all set-associate structures that are more efficient and scalable than fully associative structures.Experiments show that this simpler and faster design outperforms a conventional load/store queue based design and the NoSQ design on most benchmarks by 10.22% and 8.71% respectively.     

基于提升小波变换的冲击性负荷电能表设计

分析了提升小波变换的分解原理,提出了基于提升小波变换的冲击性负荷电能计量新方法,并进行了冲击性负荷电能表的硬件设计。与传统电能表相比,该电能表能适应冲击性负荷情况下电流动态范围大、频带宽的测量特点。通过仿真实验,论证了该方法的可行性,并证明了该方法在实时处理速度上要优于第一代小波变换。     

Fuzzy-Pareto-dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization

This paper presents a fuzzy-Pareto dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization (MOPSO). This multi-objective planning model captures the possibilistic variations of the system loads using a fuzzy triangular number. The MOPSO based on the Pareto-optimality principle is used to obtain a set of non-dominated solutions representing different network structures under uncertainties in load demands and these non-dominated solutions are stored in an elite archive of limited size. Normally, choosing the candidate non-dominated solutions to be retained in the elite archive while maintaining the quality of the Pareto-approximation front as well as maintaining the diversity of solutions on this front is very much computationally demanding. In this paper, the principles of fuzzy Pareto-dominance are used to find out and rank the non-dominated solutions on the Pareto-approximation front. This ranking in turn is used to maintain the elite archive of limited size by discarding the lower ranked solutions. The two planning objectives are: (i) minimization of total installation and operational cost and (ii) minimization of risk factor. The risk factor is defined as a function of an index called contingency-load-loss index (CLLI), which captures the effect of load loss under contingencies, and the degree of network constraint violations. The minimization of the CLLI improves network reliability. The network variables that are optimized are: (i) number of feeders and their routes, and (ii) number and locations of sectionalizing switches. An MOPSO (developed by the authors), based on a novel technique for the selection and assignment of leaders/guides for efficient search of non-dominated solutions, is used as the optimization tool. The proposed planning approach is validated on a typical 100-node distribution system. Performance comparisons between the planning approaches with the possibilistic and deterministic load models are provided highlighting the relative merits and demerits. It is also verified that the proposed solution ranking scheme based on the fuzzy-Pareto dominance is very much better from both quality and computational burden point of view in comparison with the other well-known archive truncation techniques based on clustering and solution density measurement etc.     

核电站反应堆保护系统防共因故障设计研究

共因故障是进行核电站保护系统设计时需重点关注的风险.为了使保护系统设计能避免共因故障的发生或减轻共因故障的后果,分析了共因故障的产生原因,并对国际法规、标准的相关要求进行了研究,总结出为应对共因故障所应遵循的设计准则.最后,设计了可防止共因故障的典型反应堆保护系统架构.该保护系统架构所采用的纵深防御和多样性设计手段可作为今后核电站保护系统设计的参考.     

铁路机车远程监测与故障诊断系统设计

传统的机车车载故障诊断装置在进行在线监测与车载诊断时存在实时性差、信息难以共享等不足,为此,利用车载嵌入式数据采集技术、GPRS无线传输技术、故障诊断专家系统、MQ传输技术及基于浏览器/服务器模式的客户端展现方式,设计实现一个铁路机车远程监测与诊断系统。应用结果表明,该系统性能稳定可靠,可为现代化机车管理和检修提供技术支撑。     

飞行器突发故障演化模型设计与仿真

飞行器突发故障是一种随机故障,常对飞行器的安全飞行造成严重威胁。为认识和揭示飞行器突发故障的演化规律,提出利用耗散结构理论对飞行器突发故障的演化机理、演化过程及演化特征进行分析和研究,根据时变熵和时变频带能量分布建立飞行器突发故障演化模型,并以飞机发动机喘振为例进行仿真验证。结果表明,耗散结构熵值的变化和时变频带能量的波动,能够反映飞行器突发故障的演化过程和演化特征。     

基于粒子群优化的Wv-SVM燃气负荷预测

针对传统预测模型精度不高的问题,提出基于小波核支持向量机的复合预测模型。采用小波分析提取燃气负荷相关的特征值,通过粒子群优化算法确定小波核支持向量机的参数,利用支持向量机(SVM)解决非线性回归和时间序列问题。实验结果证明,该预测模型的预测精度比BP神经网络和传统高斯核SVM高。