射线追踪中与地形的加速技术研究

在采用射线追踪方法的仿真软件中，一般都会判断射线与地形的交点，从而判断射线与哪块地形发生相交和反射、绕射。目前地形多采用三角面的形式来模拟。而一条射线对较大地形所有三角面进行一次相交的判定的过程较长。就射线与地形判断相交和确定交点的位置的过程中估计射线路径从而减少与三角面的相交判断从而进行加速，从而使得射线追踪时间能够大幅度的缩短，提高了软件仿真的效率。     

Optimal power flow model incorporating wind,solar, and bundled solar-thermal power in the restructured Indian power system

A new optimal power flow model for wind, solar, and solar-thermal bundled power scheduling and dispatch is proposed, incorporating the deviation incentive/penalty charges for renewable energy introduced in India. The multiobjective function is solved using the flower pollination algorithm; the scheme is successfully tested on the IEEE 30-bus and Indian utility 30-bus systems. The forecasting error constraints introduced in renewable energy scheduling and dispatch are demonstrated to be beneficial in several aspects. Solar-thermal bundling is shown to create win-win situations for thermal and solar generators. The effectiveness of the flower pollination algorithm in solving optimal power flow models is proved.     

基于遗传神经网络模型的空气能见度预测

由于雾霾导致的空气能见度降低,给人们的出行带来很多不便。针对这一现象,构建基于遗传神经网络算法的空气能见度预测模型。将与空气能见度相关的7种气象因子和6种污染物浓度因子经过主成份分析后作为输入数据,输出8:00能见度和14:00能见度。该模型能够克服BP神经网络易陷入平坦区域和局部最优解的问题。以西安市2013-1-1—8-16的数据训练遗传神经网络,通过使用灰色模型获得预测时间段8-17—23的输入数据,可以得到这段时间能见度的预测值。通过与BP神经网络模型的比较,发现遗传神经网络预测模型在预测结果的相关性和绝对误差方面均优于BP神经网络模型,因此,可以更准确地预测空气能见度。     

微卫星标记技术在淡水腹足类种群遗传学研究中的应用

综述了淡水腹足类微卫星位点的获取方法、微卫星重复序列的特点以及微卫星标记技术在其种群遗传学研究中的应用,着重分析了微卫星多态性在淡水腹足类种群遗传多样性、种群遗传结构、遗传分化、交配系统、种群关系研究中的应用.淡水腹足类普遍表现为杂合体缺失,种群遗传多样性降低.淡水腹足类近交和自体受精现象比较频繁以及地理隔离导致种群间遗传分化.利用微卫星标记研究遗传杂合度,结合群体近交系数和基因分化系数在一定程度上反映了淡水腹足类的交配系统.根据不同种群的遗传距离和相似性进行聚类分析在判别种群间的亲缘关系上起到了很大作用.针对淡水腹足类自身的特性,微卫星分子标记技术在淡水腹足类入侵生物学、分类阶元,寄生虫的分子流行病学等研究领域有广泛的应用前景.     

Efficient thinning regimes for Eucalyptus fastigata: Multi-objective stand-level optimisation using the island model genetic algorithm

A stand-level optimisation problem formulated to determine a set of efficient thinning regimes satisfying two objectives, i.e. value production for sawlog harvesting and volume production for a pulpwood market, was demonstrated for a Eucalyptus fastigata trial in Kaingaroa Forest, New Zealand. Genetic algorithms were used to estimate the set of efficient thinning regimes (i.e. regimes that occur when it is not possible to increase the achievement of one objective without reducing another) known as a Pareto frontier. Each thinning regime specified a unique combination of initial planting density; frequency, timing and intensity of thinning; final crop number; and rotation length. Specifications for the “best” regime in the Pareto set (i.e. the one that satisfied a balanced trade-off between value and volume production) were similar to those recommended through professional judgment based on pooled long-term field observations from different eucalypt species grown throughout New Zealand. The advantage of Pareto optimality was the ability of not only identifying a unique thinning regime, but equally efficient regimes each providing a different combination of value and volume production. Research on this approach has the potential of being applied to other forest sites, providing there is sufficient re-measurement data to reflect stand growth dynamics.     

雷电灾害风险评估系统开发与应用

根据国标《GB/T21714.2-2008雷电防护》的雷电灾害风险评估原理和方法,结合国标《GB50057-1994(2000年版)建筑防雷设计规范》和《GB50343-2004建筑物电子信息系统防雷技术规范》的相关方法,利用Del-phi7.0开发了集评估项目地理位置定位、闪电资料分析、建筑物评估和区域评估等功能的综合性雷电灾害风险评估系统。介绍了系统的需求分析、功能、特点和其中涉及的风险评估算法。利用该系统可快速、方便地完成各类项目雷电灾害风险评估工作,并自动生成word报告。     

Comparison of Aerosol Size-Distributions Using Linear-Regression, Genetic Algorithm, and Annealing Genetic Algorithm

Abstract Stability and accuracy of retrieving the aerosol size-distribution by using the linear-regression (LR), genetic algorithm (GA), and annealing genetic algorithm (AGA) were studied in detail. It was found that by using the AGA, retrieval results are quite stable and accurate; using the GA, stability and accuracy of the retrieval results decreased mildly but were still acceptable; and using the LR, retrieval results were too unstable to be acceptable. This conclusion is verified by both of the numeric simulation and the experiment.     

Optimization of test interval for ageing equipment: A multi-objective genetic algorithm approach

Reducing the unavailability of safety systems at nuclear power plants, by utilizing the probabilistic safety assessment (PSA) methodology, is one of the prime goals in the nuclear industry. In that sense, optimization of test and maintenance activities, which are defined within the technical specifications, represents quite popular and interesting domain. Obtaining optimal test and maintenance schedule is of great significance for improving system availability and performance as well as plant availability in general.On the other side, equipment aging has gradually become a major concern in the nuclear industry since the number of safety systems components, that are approaching their wear-out stage, is rising fast. Nuclear power plants life management programs, considering safety components aging, are being developed and employed. The immense uncertainty associated to the available component aging rates databases poses significant difficulties in the process of incorporation and quantification of the aging effect within the PSA and, subsequently, in the decision-making process.In this paper, an approach for optimization of surveillance test interval of standby equipment with highly uncertain aging parameters, based on genetic algorithm technique and PSA, is presented. A standard standby safety system in nuclear power plant is selected as a case study. A Monte Carlo simulation-based approach is used to assess uncertainty propagation on system level. Optimal test interval is derived on the basis of minimal system unavailability and minimal impact of components aging parameters uncertainty. The results obtained in this application indicate the fact that risk-informed surveillance requirements differ from existing ones in technical specifications as well as show the importance of considering aging data uncertainties in component aging modeling.     

Dependability assessment of network-based safety-related system

Advances in electronic integration and radio communication have led to the emergence of a new kind of safety systems, i.e. Wireless Sensor Network (WSN). This network-based safety-related system is becoming more and more present in the domain of safety due to its easy deployment. It does not need a wire infrastructure and its range of applications is wide. Usually, such a system is composed of various nodes (sensors) collaborating to monitor a targeted phenomenon. In most cases, nodes are battery powered and this is the weakness of the system makes it necessary to design an energy saving policy. The present paper gives a dependability viewpoint of such a system. A modeling framework is suggested integrating the interdependency of the components. Stochastic Petri nets are used to implement this model and two heuristics to schedule component activity. The first is based on the proposed importance measures, the second on a multi-objective genetic algorithm. The aim is to conserve energy and so to extend WSN dependability.