A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.     

Application of ACO algorithm in protein structure prediction

The hydrophobic-polar (HP) lattice model is an important simplified model for studying protein folding. In this paper, we present an improved ACO algorithm for the protein structure prediction. In the algorithm, the "lone"ethod is applied to deal with the infeasible structures, and the "oint mutation and reconstruction"ethod is applied in local search phase. The empirical results show that the presented method is feasible and effective to solve the problem of protein structure prediction, and notable improvements in CPU time are obtained.     

Effective prediction of DEA model by neural network

In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training algorithm.The SPDS training algorithm overcomes the drawbacks of slow convergent speed and partially minimum result for BP algorithm.Its training speed is much faster and its forecasting precision is much better than those of BP algorithm.By numeric examples,it is showed that adopting the neural network model in the forecasting of effective points by DEA model is valid.     

Improvements in seismic event locations in a deep western U.S. coal mine using tomographic velocity models and an evolutionary search algorithm

Methods of improving seismic event locations were investigated as part of a research study aimed at reducing ground control safety hazards. Seismic event waveforms collected with a 23-station three-dimensional sensor array during longwall coal mining provide the data set used in the analyses. A spatially variable seismic velocity model is constructed using seismic event sources in a passive tomographic method. The resulting three-dimensional velocity model is used to relocate seismic event positions. An evolutionary optimization algorithm is implemented and used in both the velocity model development and in seeking improved event location solutions. Results obtained using the different velocity models are compared. The combination of the tomographic velocity model development and evolutionary search algorithm provides improvement to the event locations.     

火电机组厂级负荷优化算法的比较

分析了目前国内火电机组厂级机组间负荷优化分配算法的研究现状.针对国内常见的火电机组类型和组合方式,在对常用算法进行分析和比较后,对各种算法的优劣和适用范围进行总结,并对今后火电机组厂级负荷优化的研究和应用提出展望.     

基于电网分区和遗传算法的配电网无功规划优化

根据电力系统的特点,结合Bender’s分解法和电网分区、灵敏度排序等方法对无功规划优化问题进行理论和应用研究,并计及不同负荷下的运行方式,以求获得较优的规划方案．最后利用IEEE30节点系统进行仿真计算,并对结果进行了分析．     

一类广义集值强非线性混合拟变分不等式(英文)

引进一类广义集值强非线性混合拟变分不等式,并且用投影技巧证明了它解的存在性.推广了广义集值强非线性混合变分不等式的许多结论.     

改进型CMA-DFE盲均衡算法的研究

CMA-DFE盲均衡算法在时变信道中有着广泛的应用.针对CMA-DFE盲均衡算法的缺点,提出一种改进型CMA-DFE盲均衡算法.该算法把CMA-DFE盲均衡算法中前馈滤波器和反馈滤波器更新方程中的迭代步长由原来的固定值改成可变值,从而进一步提高了CMA-DFE盲均衡算法的收敛性.仿真实验结果表明,与CMA-DFE盲均衡算法相比,改进型CMA-DFE盲均衡算法具有更好的均衡效果和收敛性能.     

基于四阶累积量的波束域MUSIC方法

在非白噪声背景下,基于二阶统计量的高分辨方法性能较差.基于四阶累积量的高分辨方法能较好地抑制空间高斯噪声,但其运算量较大.为了解决这一矛盾,文章提出了一种基于四阶累积量的波束域MUSIC方法.仿真分析和实验结果表明,与阵元域四阶累积量MUSIC方法相比,论文所提方法降低了分辨门限,减小了估计偏差和均方根误差,同时减小了运算量.     

一种新的求解移动Agent路由问题优化算法

文章对移动Agent在无线传感器网络中的路由问题进行描述,给出该问题的多约束最优非劣路径求解模型,并提出一种SA-CA算法求解移动Agent路由问题:利用模拟退火(Simulated Annealing,SA)算法接受最优单体以推动文化算法(Cultural Algorithms,CA)中信仰空间的进化,并将搜索步长作为情景知识指导群体空间最优解的搜索.这种双层结构特性减少了搜索时间,表现出较好的全局寻优性能.仿真实验也表明,与粒子群优化算法(PSO)、遗传算法(GA)的计算结果相比,提出的新算法在保证求解准确性的同时,具有更快的收敛速度和较高的可靠性,是解决无线传感器网络多约束QoS路由的有效途径,能提高整个网络的性能.