基于自适应搜索的免疫粒子群算法

经典粒子群算法由于多样性差而陷入局部最优,从而造成早熟停滞现象.为克服上述缺点,本文结合人工免疫算法,提出一种基于自适应搜索的免疫粒子群算法.首先,该算法改善了浓度机制;然后由粒子最大浓度值来控制子种群数目以充分利用粒子种群资源;最后对劣质子种群进行疫苗接种,利用粒子最大浓度值调节接种疫苗的搜索范围,不仅避免了种群退化现象,而且提高了算法的收敛精度和全局搜索能力.仿真结果表明该算法求解复杂函数优化问题的有效性和优越性.      

Critical Review of Aerosol Particle Transport Models for Building HVAC Ducts

Literature on aerosol behavior in conventional heating ventilating and air conditioning duct systems that is directly applicable to modeling and simulation of deposition and resuspension of particulate matter is reviewed. Open literature discussing particle resuspension from duct surfaces is rare and most studies investigating particle deposition consider only clean duct surfaces. There is a similar lack of studies on particle deposition and resuspension in mechanical components found in air distribution systems (e.g., heat exchangers, fans, dampers, etc.) and in duct flows where the turbulent velocity profile is not fully developed. An example calculation of penetration through a simple duct system illustrates the characteristics of results produced by the identified modeling methods.     

基于改进粒子群优化算法的机器人路径规划

提出了一种改进粒子群优化算法的移动机器人路径规划方法。该方法首先将粒子群分成两组,对其中一组加入变异算子,能提高种群的多样性和避免粒子群优化算法的早熟。该方法模型简单,算法复杂度低,收敛速度快。仿真实验结果获得了从起点到终点的无碰撞路径,证实了该方法的有效性和可行性。     

一种改进的人工蜂群算法——粒子蜂群算法

针对经典人工蜂群算法收敛速率较慢,后期易陷入局部最优解的不足,本文将粒子群算法中"全局最优"的思想引入到人工蜂群算法的改进过程,从而形成了一种新的人工蜂群改进算法——粒子蜂群算法.首先,提出了趋优度的概念,用来衡量引领蜂在有限次迭代过程中向全局最优解靠近或远离的程度,趋优度值可以评价个体的"发展潜力",趋优度值越低的个体,越需要增大变异的程度,以便找到质量更优的解.其次,专门设计了一种新的蜜蜂群体——粒子蜂,在引领蜂变异阶段根据趋优度的大小将引领蜂变异为侦查蜂和粒子蜂,粒子蜂的出现在很大程度上增加了种群的多样性,拓展了算法的搜索范围.然后,通过粒子蜂群算法种群序列是一个有限齐次马尔科夫链和种群进化单调性的分析,验证了本文所提算法的种群序列依概率1收敛于全局最优解集.最后,将本文所提算法应用于多个常见测试函数,并与经典蜂群算法、近年其他文献改进蜂群算法进行了仿真对比研究,仿真结果表明本文所提算法确实加大了种群的分散度、扩宽了搜索范围,从而具有更快的收敛速度和更高的寻优精度.      

Application of Improved PSO Algorithm in Hydraulic Pressing System Identification

 In view of characteristics of particle swarm optimization (PSO) algorithm of fast convergence but easily falling into local optimum value, a novel improved particle swarm optimization algorithm is put forward, and it is applicable to identify parameters of hydraulic pressure system model in strip rolling process. In order to maintain population diversity and enhance global optimization capability, the algorithm is firstly improved by means of decreasing its inertia weight linearly from the maximum to the minimum and then combined with chaotic characteristics of ergodicity, randomness and sensitivity to initial value. When the improved algorithm is used to identify parameters of hydraulic pressure system, the comparison of simulation curves and measured curves indicates that the identification results are reliable and close to actual situation. A new method was provided for hydraulic AGC system model identification.     

Effect of Sediment pH on Resuspension of Kaolinite Sediments

The resuspension of contaminated cohesive sediments can impact water quality by mobilizing sediment particles and adsorbed contaminants. Changes in psysicochemical and electrochemical environments, around resuspended sediment particles, may cause some contaminants to desorb into the water column. The contribution of contaminated sediments to degradation of water quality depends on an estimate of sediment resuspension rates. In this study, the resuspension of Georgia kaolinite sediments under varying pH conditions was investigated in laboratory flume experiments. Because the edge charge of kaolinite particles is pH dependent, kaolinite particles exhibit different modes of particle associations under varying pH conditions. The paper characterizes these particle associations and relates them to the resuspension of kaolinite sediments for varying pH values. Variations in sediment water content, changes from a stratified to a uniform sediment bed, changes in rheological properties, and variations in the electrophoretic mobility of kaolinite particles were all indicative of the changes in particle associations that resulted from changes in sediment pH. The critical shear stress and the erosion rate coefficient were evaluated for varying pH values and explained by particle associations. A rheometer was used to measure rheological properties of the settled sediment bed; the measured yield stresses had a direct correlation with critical shear stresses.     

Fluid-Particle Interactions and Resuspension in Simple Shear Flow

The lattice Boltzmann method (LBM) is used to simulate the particle resuspension process in a two-dimensional simple shear flow. At first, the lift force on a single disk-shaped particle attached at the channel wall is computed. It is found that when the particle is allowed to freely move in the viscous fluid, the resulting lift force is smaller than that when the particle is constrained to be stationary. The bulk properties of fluids with particles suspended under various concentrations are numerically calculated. The results agree reasonably well with analytical results by Batchelor when the volume fraction is lower than 50%. The resuspension process of a group of particles (up to 500) is simulated at different particle-to-fluid density ratios. It is found that the height and shape of particle bed depend on the particle density ratio and flow conditions. Interactions of groups of particles as well as the final shape of the bedform of the particles were studied during this resuspension process. Finally, the pressure distribution and flow above the bedform of particles was examined. The results obtained agree well with those observed in naturally occurring bedforms of sediments.     

基于DBN-LSSVM的热连轧带钢厚度预测

为提高热连轧带钢精轧厚度预测精度,建立了通过深度置信网络(deep belief network,简称DBN)提取特征的最小二乘支持向量机回归模型(DBN-LSSVM),并且利用粒子群优化算法(particle swarm optimization,简称PSO)优化最小二乘支持向量机(least squares support vector machine,简称LSSVM)相关超参数。通过采集某热连轧带钢生产线实时现场数据,对所提出厚度预测模型进行训练和离线仿真。结果表明,基于DBN-LSSVM的预测模型具有良好的学习能力和泛化性,DBN-LSSVM模型的预测精度较传统BP算法和DBN-BP算法有显著提高,该厚度预测模型在生产实践中具有很好的应用前景。     

Pre-alculation optimization of finishing delivery temperature of hot strip head based on data driven

The pre-alculation of finishing delivery temperature of strip head is an important content in finishing rolling setting. It is not only the basis of temperature control for the whole length of the strip, but also related to the preset parameters of rolling speed, rolling force and roll gap. It plays a key role in the quality control of finished strip steel. In practical production, the finishing delivery temperature of strip head mainly depends on semi mechanism model which combines statistical experience and adaptive correction. However, due to the complexity of the strip heat transfer process, the temperature cannot be calculated accurately by the semi mechanism model, which leads to the low accuracy of the pre calculation of finishing delivery temperature at the head of the strip. In order to solve this problem, a hybrid optimization model from a data driven point of view which based on the semi mechanism models and supplemented by BP neural network and improved particle swarm optimization (IPSO) algorithm was established. Through simulation experiment, the results show that: compared with a pure neural network model or mechanism model, the pre calculation accuracy and convergence speed of the hybrid optimization model have been greatly improved up to 9667%, and it has a good application prospect.     

数据驱动的热轧带钢头部终轧温度预计算优化

摘要：带钢头部终轧温度(finishing delivery temperature,FDT)的预计算是精轧设定中的一项重要内容。它不仅是带钢全长终轧温度控制的基础,而且关系到轧制速度、轧制力以及辊缝等模型参数的预设定,对成品带钢的质量控制起着关键性的作用。在实际生产中,带钢头部终轧温度主要是通过结合了统计经验和自适应修正的半机理模型来计算,但是由于带钢换热过程的复杂性难以用关系式精确表达,导致了带钢头部终轧温度的预计算精度不高。针对此问题,从数据驱动的角度出发,利用BP神经网络和改进粒子群优化算法（improved particle swarm optimization, IPSO）,以半机理模型为主,IPSO-P网络模型为辅,建立了一种混合优化模型。通过仿真实验和实际生产对比,结果表明：相比于单纯的神经网络模型或者半机理模型,混合优化模型的预计算精度和收敛速度均有了很大的提高,达到9667%,具有良好的应用前景。     

无数学模型的非线性约束单目标系统优化方法改进

为提高无法准确建立数学模型的非线性约束单目标系统优化问题的寻优精度,并考虑获取样本的代价,提出一种基于支持向量机和免疫粒子群算法的组合方法(support vector machine and immune particle swarm optimization,SVM-IPSO).首先,运用支持向量机构建非线性约束单目标系统预测模型,然后,采用引入了免疫系统自我调节机制的免疫粒子群算法在预测模型的基础上对系统寻优.与基于BP神经网络和粒子群算法的组合方法(BP and particle swarm optimization,BP-PSO)进行仿真实验对比,同时,通过减少训练样本,研究了在训练样本较少情况下两种方法的寻优效果.实验结果表明,在相同样本数量条件下,SVM-IPSO方法具有更高的优化能力,并且当样本数量减少时,相比BP-PSO方法,SVM-IPSO方法仍能获得更稳定且更准确的系统寻优值.因此,SVM-IPSO方法为实际中此类问题提供了一个新的更优的解决途径.      

Thermal comfort in offices:comfort values and optimization of indoor control variables

In order to search for reasonable air-conditioned indoor control variables and save energy consumption and meet tO need of personal thermal comfort,a method which is based on numerical simulation is employed to optimize indoor control variables.Computational fluid dynamics(CFD)is used to describe thermal state of office.An optimal method is proposed in this paper,dual neural network model is firstly used to acquire reliable information,data from CFD model are pre-processed,and the remaining data are used to train artificial neural networks(ANN),then CFD model is replaced by ANN model to reduce computational cost when is optimized,indoor control variables are optimized by genetic algorithm.Simulation results show that indoor thermal comfort is improved obviously,and the energy cost is decreased accordingly.     

A new coincidence model for single particle counters, part III: realization of single particle counting accuracy

U.S.P. objective tests for particle contamination in injectable fluids are based on counts of single particles in a specified test volume. Accuracy standards for these tests must therefore be based on single particle count accuracy. A definitive analysis for this purpose is described whose results can be used during a counting experiment. To improve the accuracy of particle counter data, U.S.P.XXIII has added a particle counter accuracy requirement defined in terms of a maximum particle concentration for 10 microns particles at which there is a 10% ratio of coincident occurrences. The 10% coincident count ratio cannot be directly measured: it must be calculated from experimental results using a model of the counting process. The U.S.P.XXIII count accuracy specification relies on vendor statements without definition of the methodology or model to be employed. The model of particle counting described in the literature is the Geometric Poisson model due to Jaenicke (4) and extended by Lieberman (5). Recent publications (1, 2) have shown that calculations based on this model do not agree with experimental data. This conclusion is supported and extended in this paper. The single particle counting error estimate for U.S.P.XXIII (788) SVI (3), using Jaenicke's Geometric model to evaluate a good commercial laser sourced detector, is 9.32%; the single particle count error estimate for this detector using the experimentally validated Particle-Triggered Poisson model is 19%. The count error for the concentration calculated with the Jaenicke Geometric model for the same detector is 40.5% when calculated with the validated Particle-Triggered Poisson model. The estimated count error increases for particles larger than 10 microns. Light extinction particle counters are well behaved instruments fully capable of the workhorse task of making accurate, routine single particle contamination measurements in injectable products. In principle, any particle counter instrument now in use, operated within calculated particle size and concentration contours, can deliver accurate single particle counting data. Operation within these limits both within and below the U.S.P.XXIII (788) (3) size range will assure single particle count accuracy without the injection of false counts or undercounts. These count limits vary with particle size and are determined by the capability of the counter. No single particle test can characterize the complex particle size and concentration response of a detector. In practice, selection of a counter with sufficient capability to provide the desired accuracy without constant dilution is an important consideration. When particle concentration exceeds the selected count accuracy contour, dilution and a repeat of the assay provide a practical solution.     

改进人工鱼群算法及其在时滞系统辨识中的应用

针对人工鱼群算法(AFSA)存在收敛速度慢和寻优精度低等问题,本文提出了一种改进人工鱼群算法(IAFSA).该算法中的人工鱼能够根据鱼群当前状态调整自身的视野和步长来平衡局部搜索和全局搜索.此外,算法中还加入了引导行为,即人工鱼在觅食行为未发现更优的位置时,当前人工鱼向最优人工鱼移动一步.仿真结果表明,改进人工鱼群算法在收敛速度、寻优精度和克服局部极值等方面有很大优势.本文将改进鱼群算法应用时滞系统的辨识中,辨识结果表明改进算法能获取被控对象的精准数学模型,并具有较强的抗干扰能力.      

Particle Network Model for Simulating the Filtration of a Microfine Cement Grout in Sand

This paper describes the application of a quasi-one-demensional (1D) network, bubble model to simulate the filtration of a microfine cement suspension injected through a column of sand. The bubble model represents the soil column as a series of homogeneous sites linked through bundles of cylindrical bonds that characterize the distribution of pores. The model captures mechanisms of particle sieving at the inlet sites and infiltration within each of the bonds under the action of hydrodynamic and gravitational forces. The behavior of each bond is based on numerical simulations of particle transport, collection, mounding, and clogging within a cylindrical model pore. Two empirical parameters are introduced to account for uncertainties in particle attachment at the pore wall and observed mechanisms of resuspension of hindered particles. In principal, these parameters can be calibrated from measurements of effluent concentrations. The bubble model provides useful physical insights to explain the transient dynamics of filtration processes. The model is able to provide consistent predictions of pore pressures measured in 1D column injection tests reported in the literature.     

多目标粒子群优化算法研究综述

针对多目标粒子群优化算法的研究进展进行综述。首先,回顾了多目标优化和粒子群算法等基本理论;其次,分析了多目标优化所涉及的难点问题;再次,从最优粒子选择策略,多样性保持机制,收敛性提高手段,多样性与收敛性平衡方法,迭代公式、参数、拓扑结构的改进方案5个方面综述了近年来的最新成果;最后,指出多目标粒子群算法有待进一步解决的问题及未来的研究方向。      

Prospective comparison of single-zone and multizone laser in situ keratomileusis for the correction of low myopia

OBJECTIVE: This study aimed to determine whether there is a true difference in predictability between single-zone and multizone laser in situ keratomileusis (LASIK) for the correction of low myopia or whether any difference in predictability is because of a need for separate clinical nomograms for the two treatments. DESIGN: A prospective, randomized, clinical trial. PARTICIPANTS: Fourteen surgeons and 190 eyes of 95 patients with myopia who desired surgical correction of myopia ranging from -2.00 to -7.00 diopters (D) participated. INTERVENTION: Fellow eyes of patients with myopia undergoing bilateral simultaneous LASIK were randomized to single-zone and multizone ablation. Astigmatism was not corrected at the time of LASIK. MAIN OUTCOME MEASURES: Predictability, postoperative videokeratography, and contrast sensitivity were assessed at 3 months after surgery. RESULTS: At 3 months, r2 values relating laser setting to change in spherical equivalent refraction were 0.78 for single-zone and 0.76 for multizone ablation; mean outcome with respect to intended was -0.84 D (0.65 standard deviation [SD]) for single-zone and -0.62 D (0.78 SD) for multizone eyes (P = 0.035). There was no relationship between single-zone and multizone ablation and the likelihood of asymmetric postoperative videokeratography (P = 0.83). The only difference in contrast sensitivity was a significantly greater decrease in log contrast for multizone eyes at 12 cyc/deg under undilated conditions. CONCLUSIONS: There is no true difference in predictability between single-zone and multizone LASIK for the correction of low myopia. Separate clinical nomograms for single-zone and multizone LASIK should eliminate the difference in predictability that was observed in this clinical trial.     

含有自校正模型的加权多模型自适应控制

研究了含有大范围参数不确定性离散时间被控对象的加权多模型自适应控制问题(包括模型集构建和加权算法分析).通过构建含有自校正模型和多个固定模型的模型集覆盖并逼近被控对象,在模型输出误差可分的前提下,采用基于模型输出误差性能指标的加权算法,并依据固定模型中是否包含真实被控对象模型的不同情形分析加权算法的收敛性.在权值收敛的前提下,利用虚拟等价系统理论,分析了参数未知线性时不变和参数跳变的情形,在不依赖于特定局部控制算法的基础上,证明了此种模型集构建下的加权多模型自适应控制系统的稳定性和收敛性,放宽了先期加权多模型自适应控制系统稳定性分析中关于模型集构建的约束条件.最终,通过计算机MATLAB仿真,验证了此类加权多模型自适应控制系统的收敛性和闭环稳定性.      

Role of monensin PLGA polymer nanoparticles and liposomes as potentiator of ricin A immunotoxins in vitro

Monensin is a carboxylic ionophore which can potentiate the activity of ricin based immunotoxins (IT) in vitro and in vivo against a variety of human tumours. Monensin was encapsulated into nanoparticles (NP) by using biodegradable poly(DL-lactide-co-glycolide) (PLGA, 50:50). The NP were prepared by modified emulsification-solvent evaporation method. High shear homogenization followed by simultaneous stirring and bath sonication were used for preparing NP. The size of NP was determined by photon correlation spectroscopy using a BI 90 particle sizer (Brookhaven Instruments). The average size of NP could be decreased from 567 nm to 163 nm by increasing the concentration of polyvinyl alcohol from 10% to 100% of PLGA. The NP were spherical in shape as observed by Atomic Force Microscopy. The concentration of monensin in the NP was analyzed by HPLC and the entrapment efficiency was found to be more than 12%. The zeta potential of NP was -25.8 (+/- 1.3) mv, which did not change significantly after resuspension of the freeze dried sample. The NP were tested against HL-60 and HT-29 human tumour cell lines in vitro. Monensin NP potentiated the activity of IT by 40 to 50 times against these cell lines. There was however, no difference between the NP and liposomes for their potentiating affect of IT against the two tumour cell lines.     

基于改进遗传算法优化Elman网络的板形识别方法

针对目前的板形缺陷识别方法精度不高、识别速度慢的问题,根据Elman神经网络模型可以反映系统动态特性,而且可以逼近任意非线性函数的特点,提出了一种利用改进的遗传算法优化Elman神经网络,使其泛化能力强、学习速度快、识别精度高,并建立板形缺陷模式识别模型的方法。为了验证该方法的识别能力,在隐层节点数与学习次数相同的条件下,分别与遗传算法优化的Elman网络和BP网络模型进行板形识别仿真对比分析。试验结果表明,改进遗传算法优化的Elman神经网络模型对板形缺陷识别精度高于BP网络等模型,并且具有收敛速度快的优点。