小波域多尺度ARMA模型的纹理分割方法

纹理特征的提取是纹理分割的关键。在小波域内,对图像建立多尺度自回归滑动平均(MultiscaleAutoregressiveMovingAverage,简称MARMA)模型,并用小生境遗传算法对MARMA模型进行参数估计,将估计出的参数向量作为纹理分割的特征标准。不同纹理图像的MARMA模型的参数向量具有较好的可分性,这就保证了最终纹理分割的质量。     

In Vitro and In Vivo Modeling of Normal and Leukemic Bone Marrow Niches: Cellular Senescence Contribution to Leukemia Induction and Progression

Cellular senescence is recognized as a dynamic process in which cells evolve and adapt in a context dependent manner; consequently, senescent cells can exert both beneficial and deleterious effects on their surroundings. Specifically, senescent mesenchymal stromal cells (MSC) in the bone marrow (BM) have been linked to the generation of a supporting microenvironment that enhances malignant cell survival. However, the study of MSC’s senescence role in leukemia development has been straitened not only by the availability of suitable models that faithfully reflect the structural complexity and biological diversity of the events triggered in the BM, but also by the lack of a universal, standardized method to measure senescence. Despite these constraints, two- and three dimensional in vitro models have been continuously improved in terms of cell culture techniques, support materials and analysis methods; in addition, research on animal models tends to focus on the development of techniques that allow tracking leukemic and senescent cells in the living organism, as well as to modify the available mice strains to generate individuals that mimic human BM characteristics. Here, we present the main advances in leukemic niche modeling, discussing advantages and limitations of the different systems, focusing on the contribution of senescent MSC to leukemia progression.     

An Ecological Perspective of Listeria monocytogenes Biofilms in Food Processing Facilities

Listeria monocytogenes can enter the food chain at virtually any point. However, food processing environments seem to be of particular importance. From an ecological point of view, food processing facilities are microbial habitats that are constantly disturbed by cleaning and sanitizing procedures. Although L. monocytogenes is considered ubiquitous in nature, it is important to recognize that not all L. monocytogenes strains appear to be equally distributed; the distribution of the organism seems to be related to certain habitats. Currently, no direct evidence exists that L. monocytogenes-associated biofilms have played a role in food contamination or foodborne outbreaks, likely because biofilm isolation and identification are not part of an outbreak investigation, or the definition of biofilm is unclear. Because L. monocytogenes is known to colonize surfaces, we suggest that contamination patterns may be studied in the context of how biofilm formation is influenced by the environment within food processing facilities. In this review, direct and indirect epidemiological and phenotypic evidence of lineage-related biofilm formation capacity to specific ecological niches will be discussed. A critical view on the development of the biofilm concept, focused on the practical implications, strengths, and weaknesses of the current definitions also is discussed. The idea that biofilm formation may be an alternative surrogate for microbial fitness is proposed. Furthermore, current research on the influence of environmental factors on biofilm formation is discussed.     

基于共享免疫粒子群算法的电力系统无功优化

针对电力系统无功优化中的粒子群算法计算效率较低,而且易陷入局部最优解等问题,将共享免疫粒子群算法应用到无功优化中,该算法在进行初始化时,运用正交的思想,使得粒子分布均匀;并用记忆粒子和克隆选择来更新粒子,这样可以很好地保持优化过程中粒子群的多样性。经过算例计算证明,共享免疫粒子群算法与其他算法相比,能够及时跳出局部最优得到全局最优解,且收敛速度快、精度高。     

基于连锁环网与改进离散粒子群算法的多目标配电网重构

配电网重构本质上是一个复杂的高维数非线性组合优化问题。为避免其不可行解的影响,同时实现快速寻优,提出了一种通过连锁环网矩阵快速判断粒子是否满足配电网拓扑约束的方法。采用基于Pareto准则的离散二进制粒子群算法(Binary Particle Swarm Optimization,BPSO)以求解配电网重构多目标优化问题。从三方面对BPSO算法进行改进:改进粒子更新策略以提升新代粒子的可行概率;改进sigmoid函数同时提出邻域搜索机制以强化算法后期的收敛能力;提出基于次优解保留策略的小生境共享机制以改进群体最优粒子更新方式,进而强化算法的全局搜索能力。对IEEE33系统算例进行仿真,结果表明改进BPSO算法在求解含分布式电源(Distributed Generation,DG)的配电网重构多目标优化问题时,能够更加精确高效地收敛至Pareto最优前沿。     

基于生态位理论的虚拟企业BP神经网络评价模型研究

借鉴了生态学中生物生态位的概念和理论,分析了虚拟企业生态位概念的内涵.从虚拟企业生存、进化和竞争的三维生存空间角度,建立了基于"态"、"势"、"位"概念的虚拟企业生态位评价体系.构建了基于BP神经网络的虚拟企业生态位评价模型,利用MATLAB神经网络软件包进行仿真计算,并通过典型算例验证了该方法的有效性.     

一种求解最小诊断代价的小生境遗传算法

在诊断操作相关的情况下,求解最小代价的诊断操作序列的过程是一个NP完全问题．目前的算法在建模和求解方面都不：是十分理想．通过对诊断问题进行更精确的建模和分析,提出了求解最小诊断代价的小生境遗传算法NGAMECD（Niche Genetic Algorithm for Minimum ECD）．实验证明,算法NGAMECD具有良好的性质,它需要的空间可以预测,较普通的遗传算法具有更好的隐式并行性,执行过程中群体能够保持多样性,在有效避免早熟问题的同时算法的收敛速度较快．NGAMECD与P／C更新算法相比,诊断代价减少了20％～50％．     

Cruise missile multiple routes planning based on hybrid particle swarm optimization

In order to solve cruise missile route planning problem for low-altitude penetration,a hybrid particle swarm optimization( HPSO) algorithm is proposed. Firstly,K-means clustering algorithm is applied to divide the particle swarm into multiple isolated sub-populations,then niche algorithm is adopted to make all particles independently search for optimal values in their own sub-populations. Finally simulated annealing( SA) algorithm is introduced to avoid the weakness of PSO algorithm,which can easily be trapped into the local optimum in the search process. The optimal value obtained by every sub-population search corresponds to an optimal route,multiple different optimal routes are provided for cruise missile. Simulation results show that the HPSO algorithm has a fast convergence rate,and the planned routes have flat ballisticpaths and short ranges which meet the lowaltitude penetration requirements.     

求解高维多模优化问题的正交小生境自适应差分演化算法

针对传统优化算法在求解高维多模态优化问题时存在收敛速度慢、求解精度低的问题,提出一种基于正交设计与小生境精英策略的自适应差分进化算法ONDE。首先利用正交表产生初始种群,然后采用小生境精英策略来产生小生境种群(NP),并用小生境种群更新精英个体;接着应用拥挤裁剪避免种群陷入局部搜索,最后利用自适应差分变异算子改进了差分进化(DE)算法。通过对7个benchmark函数仿真验证,实验结果表明,算法在收敛速度、求解精度和稳定性方面都有较大优势。     

引导小生境回溯优化算法

回溯搜索优化算法（BSA）是近年提出的一种新型优化算法,针对其收敛速度较慢、易陷于局部最优的缺点,提出了一种基于最优个体引导和小生境技术相结合的改进BSA算法。本方法首先在BSA的变异操作中引入向最优个体学习的策略,以提高算法的收敛速度;其次,设计一种新的小生境排挤技术,根据每个个体到其他个体距离的平均最小值确定小生境半径,排除部分相似性较高的个体;结合群体当前的最差信息,设计一种新的变异方法产生一定数量的新个体补充到新的种群中,维持群体数量的恒定并增强群体多样性。改进的BSA算法充分考虑了算法的收敛速度和群体的多样性,较大地提高了传统BSA算法的性能。对10个典型函数进行仿真测试,并与其他算法结果进行对比,实验结果表明,改进算法在收敛速度与精度方面具有较好的效果。