Dynamic discrimination of convergence of the LMS time delay estimation in complicated noisy environments

For nonstationary signals, time constant can no longer represent the actual convergence performance of the adaptive delay estimation. A scheme is thus proposed for dynamically discriminating the convergence of the LMS time delay estimation to get an optimal estimate even under complicated noisy environments. Based on the fact that the filter output gets orthogonal to the estimation error only when an adaptive filter operates in its optimal condition, an approximate orthogonality constraint is derived for discriminating the practical adaptation’s convergence. This discrimination condition, which is obtained through the estimated mean-square values of related available variables at each iteration, can be updated in the adaptation process according to the varying statistic characteristics of observed signals. Thus, it can dynamically evaluate the quality of the adaptive solution and end the iteration opportunely. Simulation results are also included to demonstrate its effectiveness to give exact convergence results in both stationary and nonstationary cases.     

Study on the method and system implementation of an isolated word recognition system used in noisy environment

I.IntroductionRecentlytherearemanykindsofsystemsandproductsforspeechrecognition,butalmostallofthemareworkinginquietenvironment,theperformancearedegradedorevencan'tworkwhenitisoperatedinhighnoisyenvironmentssuchasincockpits,vehicle,workshopsetc.SonoiserobustnesshasbecomeoneofthemainobstaclesfortherealaPplicationsoftheautomaticspeechrecognizersanditattractstheattentionofresearchersinspeechtechnologyareas.Since1978,substantialeffortshavebeendevotedtotestandevaluatethespeechrecognizersusedinfight…     

Using genetic algorithms to characterize ferrofluid topographies in externally applied magnetic fields

Both ferrofluidics and genetic algorithms are relatively new fields. Due to complex physical interactions, ferrofluidic topographies and assemblies have only been solved using finite time step, Lattice Boltzmann, and finite-element methods in very simple magnetic field configurations. In this paper, we show that it is possible (and highly advantageous) to employ genetic algorithms to solve for the fluid topographies, which can be extended to include more complex magnetic fields.     

基于遗传算法的过热度模糊控制因子优化研究

模糊控制器的量化因子和比例因子对蒸发器过热度的调节品质影响极大。针对传统试凑法选择量化因子和比例因子时主观因素强且难以实现全局最优的缺点,采用遗传算法对两类模糊因子进行优化。仿真结果表明:优化后的模糊控制器动态、静态性能均优于传统模糊控制器。     

基于遗传算法进行加速器自动调束

 介绍了遗传算法的基本理论，及其应用于加速器调束过程中的设计要点，给出基于遗传算法进行加速器自动调束的设计方法，并编写遗传算法程序针对HIRFL中的一段束流输运线进行了计算机仿真实验，证明了基于遗传算法进行自动调束的可行性，指出了自动调束技术的未来研究方向。     

Low-threshold performance of fiber Bragg grating external-cavity semiconductor lasers using genetic algorithms

The lowest threshold current of the external-cavity semiconductor laser with fiber Bragg grating using genetic algorithms is investigated. Effects of the external cavity length, coupling efficiency and anti-reflection coating reflectivity on static characteristics, such as L-I curves and side mode suppression ratios (SMSR) are investigated by using multi-mode rate equation. It is found that the reflectivity of the anti-reflection coating for low-threshold performance needs to be increased at the cost of the stability of the fiber grating external-cavity semiconductor laser. The optimal external cavity length obtained by genetic algorithms can be used to obtain the lowest threshold current for sacrificing a little SMSR. However, with the decrease of AR-coating reflectivity, output powers and SMSRs are slightly dependent on the external cavity length.     

遗传算法在超声检测反演参数中的应用

本文简单介绍了遗传算法的理论基础和计算流程,讨论它的优越性能。结合反常 纤维样品独立弹性常数的实验,探讨了遗传算法在此领域中的应用,与前反演结果相比较,验证了该算法的有效性。     

Scale-free properties of information flux networks in genetic algorithms

In this study, we present empirical analysis of statistical properties of mating networks in genetic algorithms (GAs). Under the framework of GAs, we study a class of interaction network model—information flux network (IFN), which describes the information flow among generations during evolution process. The IFNs are found to be scale-free when the selection operator uses a preferential strategy rather than a random. The topology structure of IFN is remarkably affected by operations used in genetic algorithms. The experimental results suggest that the scaling exponent of the power-law degree distribution is shown to decrease when crossover rate increases, but increase when mutation rate increases, and the reason may be that high crossover rate leads to more edges that are shared between nodes and high mutation rate leads to many individuals in a generation possessing low fitness. The magnitude of the out-degree exponent is always more than the in-degree exponent for the systems tested. These results may provide a new viewpoint with which to view GAs and guide the dissemination process of genetic information throughout a population.     

Numerical analysis of multi-wavelength cascaded Raman fiber lasers based on genetic algorithm

A novel numerical method is presented for the calculations of the coupled propagation equations in multi-wavelength Raman fiber lasers. By taking the advantages of genetic algorithm and shooting method, only a few of the best individuals at each generation are chosen to implement several shootings in order to accelerate theirs converging. The output characteristics of an all-fiber three-wavelength Raman fiber laser have been analyzed based on the proposed algorithm. Results show that the total output power linearly depends on the pump power with a slope efficiency of ∼51%. For the three output Stokes, the slope efficiencies of the longer wavelengths are larger than that of the shorter ones because the optical energy at the Stokes with shorter wavelengths allows for a transfer of optical power to the longer Stokes via stimulated Raman scattering. We also find that the total output power degrades by less than 10% by adjusting the output-coupler reflectivity and is insensitive to the variation of the Raman fiber length over a large range.     

基于遗传算法的减反射膜的优化设计

为了研究膜系设计领域内的全局性优化方法,提出了基于遗传算法的膜系设计方法。编制了用遗传算法进行膜系设计的通用程序,并在各种类型的减反射膜设计中取得了成功。结果表明：基于遗传算法得到的减反射膜具有与近期发表的膜系结果相一致或更好的光谱性能和全局性。同时在应用遗传算法进行膜系设计时,不需要给定初始膜系,可以设定膜层厚度边界和膜层数,这对于设计和镀制有现实意义的光学薄膜极为有利,并且只要改变目标反射率就能推广到其他膜系的设计中,具有良好的可移植性。     

算法对混沌体系逃逸率的影响

以Hénon-Heiles体系为例，研究算法对混沌体系运动轨道和逃逸率计算结果的影响.比较了新发现的四阶辛算法和一种非辛的高阶算法得到的结果.发现两种算法给出的轨迹之间的距离随时间增大，增加的速度可以作为体系相空间混沌的度量.通过跟踪大数量的粒子轨迹，提取出了逃逸率随体系能量的变化.发现由两种算法得到的逃逸率相互符合得很好. 关键词： 逃逸率 Hénon-Heiles体系 辛算法     

典型卫星轨道辐射环境及在轨软错误率预计模型分析

使用最新版本的Space Radiation 7.0软件对典型卫星轨道（包括地球同步轨道、中地轨道和低地轨道）的空间辐射环境进行提取和计算,分析不同空间天气和屏蔽条件下的轨道离子通量-能量谱和通量-线性能量沉积（LET）谱特点。以一款SOI SRAM为例,结合地面加速器重离子试验获得的单粒子翻转截面-LET值关系曲线,预计该器件的在轨软错误率（SER）,并分析关键参数对预计结果的影响规律和内在机理。结果表明,使用Space Radiation软件的四种输入模式获得的预计结果可相差5倍左右;灵敏区厚度的增大导致在轨SER降低数个数量级,原因为灵敏区厚度的设置与灵敏区平均投影面积和符合条件的空间离子通量的大小直接相关;漏斗长度的大小对预计结果有一定的影响。最后,对SER预计模型的适用性和发展趋势进行了讨论。     

Genetic algorithms for the QoS based multicast routing and wavelength allocation problem in WDM network

To tackle the QoS based multicast routing and wavelength allocation problem (MRWA), three multi-objective genetic algorithms are proposed, which are based on the ideas of Non-dominated Sorting, Strength Pareto and Decomposition, respectively. The chromosome coding scheme, crossover and mutation operators are redefined. To ensure the generated offspring being a connected light-tree, a light-path repair process and a loop eliminating process are designed. The proposed algorithms were evaluated on a set of different scale test problems and compared with the recently proposed GA based multi-objective optimization algorithm for this problem. The experimental results reveal very encouraging results in terms of the solution quality.     

Community detection based on modularity and an improved genetic algorithm

Complex networks are widely applied in every aspect of human society, and community detection is a research hotspot in complex networks. Many algorithms use modularity as the objective function, which can simplify the algorithm. In this paper, a community detection method based on modularity and an improved genetic algorithm (MIGA) is put forward. MIGA takes the modularity Q$Q$ as the objective function, which can simplify the algorithm, and uses prior information (the number of community structures), which makes the algorithm more targeted and improves the stability and accuracy of community detection. Meanwhile, MIGA takes the simulated annealing method as the local search method, which can improve the ability of local search by adjusting the parameters. Compared with the state-of-art algorithms, simulation results on computer-generated and four real-world networks reflect the effectiveness of MIGA.     

Temperature effect on deposition rate of silicon nitride films

Temperature effects on deposition rate of silicon nitride films were characterized by building a neural network prediction model. The silicon nitride films were deposited by using a plasma enhanced chemical vapor deposition system and process parameter effects were systematically characterized by 2⁶⁻¹ fractional factorial experiment. The process parameters involved include a radio frequency power, pressure, temperature, SiH₄, N₂, and NH₃ flow rates. The prediction performance of generalized regression neural network was drastically improved by optimizing multi-valued training factors using a genetic algorithm. Several 3D plots were generated to investigate parameter effects at various temperatures. Predicted variations were experimentally validated. The temperature effect on the deposition rate was a complex function of parameters but N₂ flow rate. Larger decreases in the deposition rate with the temperature were only noticed at lower SiH₄ (or higher NH₃) flow rates. Typical effects of SiH₄ or NH₃ flow rate were only observed at higher or lower temperatures. A comparison with the refractive index model facilitated a selective choice of either SiH₄ or NH₃ for process optimization.     

纠缠比特在不同噪声环境和信道下演化规律的实验研究

量子信息技术主要基于量子纠缠,量子纠缠源作为重要的相干叠加态,其相干性很容易受到环境的影响而变得非常脆弱,甚至导致量子信息处理的失败.因此,全面揭示不同噪声环境和不同噪声信道下量子纠缠源演化规律,进而探寻抑制退相干的方法就显得至关重要.本文以量子信息最基本的单元-两比特纠缠对作为研究对象,实验上利用线性光学系统模拟了比特翻转和相移噪声(集体和非集体),研究了纠缠源在不同噪声环境及单、双和混合噪声信道下保真度的变化规律.实验结果表明:对同一种噪声类型,当纠缠比特经过双通道噪声环境时,其纠缠特性破坏得快;当纠缠比特经过非集体环境时,其纠缠特性消失得快.对不同噪声类型比较,结果表明比特翻转噪声相对于相移噪声更容易破坏纠缠特性.所得结论对纠缠退相干的理论和实验研究具有重要的借鉴意义,同时对基于非线性光学系统的量子信息处理技术具有重要的应用价值.     

Optimization of the operational parameters in a fast axial flow CW CO2 laser using artificial neural networks and genetic algorithms

This paper presents an artificial intelligence approach for optimization of the operational parameters such as gas pressure ratio and discharge current in a fast-axial-flow CW CO₂ laser by coupling artificial neural networks and genetic algorithm. First, a series of experiments were used as the learning data for artificial neural networks. The best-trained network was connected to genetic algorithm as a fitness function to find the optimum parameters. After the optimization, the calculated laser power increases by 33% and the measured value increases by 21% in an experiment as compared to a non-optimized case.     

A genetic algorithm-based approach for solving the target Q-coverage problem in over and under provisioned directional sensor networks

Target coverage and network lifetime extension have been addressed as two major research topics over the last two decades. This paper focuses on “target $Q$-Coverage” in Directional Sensor Networks (DSNs) where coverage requirement of each target in the environment differs from that of the others. In such network, how to achieve the coverage requirement and simultaneously prolong the network lifetime is a major problem. In this study, two target-oriented genetic-based algorithms were developed to solve the problem. The first algorithm was developed to cover the targets in an over-provisioned environment, and the second algorithm was developed in an under-provisioned environment. The main objective of the first algorithm is satisfying the coverage requirement of targets by activating minimal sensors, while the second algorithm was developed to achieve a maximum balanced coverage for all the targets in the network. To evaluate the performance of the developed algorithms, they were compared with some state-of-the-art algorithms presented in recent studies. In this regard, several parameters, including Distance Index, Q-Balancing Index, Coverage Quality, Power Consumption, and Activate Sensors were taken into account. The comparative results indicated that the developed algorithms performed efficiently in solving the $Q$-coverage problem in both environments.     

Combination of genetic algorithm and computational fluid dynamics in combustion process emission minimization

A computational fluid dynamics (CFD) code was connected with a systematic optimization algorithm to find the optimum settings for nitric oxide (NO) emission minimization in the bubbling fluidized bed boiler. This approach is superior to the typical approach in which CFD is used to calculate only a few intuitively selected cases, and the best case found is treated as the ‘optimum’ solution. Ammonia injection (selective non-catalytic reduction) is applied to control NO emission in the modelled fluidized bed boiler. Design variables consisted of ammonia mass flow from distinct injections. Genetic algorithm (GA) was used for the optimization. The predicted NO emission was reduced approximately 15% with respect to the current operating point by the application of GA and CFD. The use of CFD connected with systematic optimization algorithm seems to be a promising design tool for combustion process optimization.