基于种群动态膜系统的大熊猫种群模型

大熊猫谱系数据是研究大熊猫种群动态性的重要数据基础,因此从保护大熊猫的角度考虑,大熊猫生态系统的数据建模具有重要意义。针对该问题提出了一种使用种群动态膜系统对大熊猫生态系统进行数据建模的方法。基于中国动物园协会已发布的大熊猫谱系数据从大熊猫个体行为上来模拟研究中国大熊猫保护研究中心圈养大熊猫种群特征,详细分析繁殖参数的变化规律并加入野放模块,设计了一个符合其特点的具有两层嵌套膜结构、对象集合和一系列进化规则的种群动态膜系统。在该膜系统的仿真实验中得到的全体大熊猫仿真结果与实际数据的相对误差最大不超过±4.13%,基本控制在±2.7%以内。实验结果验证了该计算模型的有效性和正确性,能够模拟预测大熊猫种群的变化趋势,给管理者的决策提供依据。     

话题

日前，台湾首富、鸿海集团塞事长郭台铭参观成都大熊猫繁育研究基地时，决定出资600万元，一次性终身认养3对大熊猫幼仔。据悉，这是该基地自1987年建立以来收到的最大笔大熊猫认养资金。     

高炉铁水质量均方根误差概率加权集成学习建模

高炉炼铁是一个物理化学反应复杂、多相多场耦合的大滞后、非线性动态密闭系统,其关键质量指标―铁水温度、Si含量、P含量、S含量难以直接在线检测,且离线化验过程滞后严重.针对这一实际工程难题,本文提出一种基于均方根误差概率加权集成学习建模算法,用于高炉多元铁水质量的预测建模.首先,为了提高建模数据质量,对高炉原始数据进行时间粒度的统一、数据归一化等数据预处理操作;为了提高建模效率和降低计算复杂度,采用灰色关联分析法提取与多元铁水质量指标关联度最强的关键变量作为建模输入变量.然后,为了提高建模的精度,提出一种均方根误差概率加权集成随机权神经网络(RVFLNs)算法.该算法采用具有快速建模速度的RVFLNs为子模型,使用核密度估计方法估计出子模型的均方根误差概率密度函数曲线,进而求出每个子模型的均方根误差概率并作为自身权重进行加权求和,得到最终的均方根误差加权集成RVFLNs模型.最后,数值仿真验证和工业试验表明:所提算法能够根据实时输入数据的变化对多元铁水质量进行快速准确的预测.     

基于“C藤”Pair Copula的高维OLAP查询建模方法研究

信息爆炸造成的数据仓库维度的急剧增加,大大影响了OLAP查询模型的精度和效率.首次将数理统计学中的“C藤”Pair Copula引入到OLAP查询建模的研究中,有效地解决了高维OLAP查询建模时的“维数灾难”问题,并设计了针对该模型的参数估计方法以提取数据概要知识.实验分析表明与传统方法相比,基于Pair Copula方法的模型可以在保证OLAP的查询精度的基础上减少数据立方体的存储空间,并且在高维数据环境下具有更高的查询效率.     

知识建模的清洗模型研究

数据清洗是保证数据质量的实用方法,但是如何实施灵活的和有效的数据清洗一直是研究的难点.基于此,提出了知识建模的数据清洗来保证数据质量的有效方法.依据清洗系统的现状分析,首先给出了静态库、动态库和静态清洗系统、动态清洗系统的相关概念;然后提出了知识建模的清洗模型,并给出了模型的具体设计和实现过程.最后通过使用知识建模的清洗模型,应用企业信用数据交换系统作为实验,验证了它的灵活性和有效性,保证了数据质量.     

基于剪枝堆栈泛化的离线数据驱动进化优化

现实世界中存在很多目标函数的计算非常昂贵, 甚至目标函数难以建模的复杂优化问题. 常规优化方法在解决此类问题时要么无从入手, 要么效率低下. 离线数据驱动的进化优化方法不需对真实目标函数进行评估, 跳出了传统优化方法的固铚, 极大推动了昂贵优化问题和不可建模优化问题的求解. 但离线数据驱动进化优化的效果严重依赖于所采用代理模型的质量. 为提升离线数据驱动进化优化的性能, 提出了一个基于剪枝堆栈泛化(Stacked generalization, SG)代理模型构建方法. 具体而言, 一方面基于异构的基学习器建立初级模型池, 再采用学习方式对各初级模型进行组合, 以提升代理模型的通用性和准确率. 另一方面基于等级保护指标对初级模型进行剪枝, 在提高初级模型集成效率的同时进一步提升最终代理模型的准确率, 并更好地指导种群的搜索. 为验证所提方法的有效性, 与7个最新的离线数据驱动的进化优化算法在12个基准测试问题上进行对比, 实验结果表明所提出的方法具有明显的优势.     

不确定数据的重复记录检测

随着不确定数据成为研究的热点,不确定数据管理吸引了研究者的极大兴趣.目前业界已经使用概率数据库来存储和管理不确定数据.为合并多个自治概率数据库中的数据,需要对不确定数据进行集成.现有对数据集成的研究主要集中于对确定数据(关系型数据和半结构化数据)的研究,对不确定性数据的集成没有相关工作.重复记录检测是集成过程中必要和具有代表性的组成部分,文中讨论了重复检测的基础,研究了有依赖和无依赖的不确定数据重复检测,最后提出了两个不确定数据重复记录检测的模型     

基于概率图模型的视频多目标跟踪

针对常见的颗粒滤波器处理视频多目标跟踪的局限性,将概率图模型引入视频多目标跟踪的分析,研究目标被遮挡跟踪中的目标不确定性。在数据关联方法的基础上,采用多目标建模和分析颗粒过滤器框架的目标关系的概率图模型,即联合数据关联算法,对闭塞视频中不确定的多目标的变化进行处理,增强数据边缘,提取目标特征。     

基于有限信息的铜吹炼动态过程智能集成建模

针对具有强动态变化特征但过程信息有限可知的铜转炉吹炼过程, 提出一种基于有限数据信息的吹炼动态过程智能集成建模方法. 依据冶金反应动力学原理, 建立了描述吹炼过程反应体系变化的非线性动力学模型; 引入动力学系数修正因子, 基于有限的数据信息和龙格–库塔公式, 构建了动力学系数修正因子的优化模型; 结合智能决策生成的典型样本集, 提出了基于微粒群算法和模式搜索法的混合智能算法确保有效获得最优修正因子, 最 终形成吹炼过程的动态模型. 用实际生产数据仿真实验, 模型预测的最大相对误差小于5%, 仿真结果验证了模型有效性.     

图数据分析系统计算模型综述

随着大数据时代的到来,图作为一种表示和分析大数据的有效方法,正成为学术界和工业界广泛关注的焦点.图数据具有规模巨大、耦合性强、动态变化等特点,需要新的计算模型支持高效的图计算系统对大规模图数据进行处理.对图计算系统计算模型的研究现状进行了调研和综述,首先介绍图计算系统的产生和发展,然后将主流图计算系统中的计算模型按照计算对象分为:结点中心计算模型、边中心计算模型、路径中心计算模型和子图计算模型四类,重点介绍结点中心模型的应用和性能,最后对图计算模型的发展过程进行总结,并展望图计算模型未来发展方向.     

Mapping the vegetation changes in giant panda habitat using Landsat remotely sensed data

Since the 1950s, with national policy changes and socio-economic development, the habitat of the giant pandas has altered accordingly. This can also be inferred from the population changes of the giant pandas as reported in three national surveys. Thus, monitoring the changes in giant panda habitat and then taking appropriate action would be a valuable contribution to giant panda protection. In this paper, using existing habitats and potential habitats of the giant pandas as the study area, multitemporal remotely sensed data from the three national surveys are used as the data source. The land cover of the study area is mapped by the maximum likelihood classification (MLC) method. The overall accuracy and kappa statistics for the resulting classification are 0.8 and 90%, respectively. The results reveal that the current status of the giant panda habitat is very good. Between 1974 and 1989, because of deforestation in the area, the giant panda habitat deteriorated considerably; the total area of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs decreased from 62.03% to 57.40% in the study area. However, from 1989 to 2002, due to the conservation policies put into action, the giant panda habitat recovered to some extent; the total area of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs increased from 57.40% to 60.68% in the study area. However, conditions are different among the mountain systems. Taking into account only the total of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs, in the Minshan mountain system, the forest cover changed from 57.70% in 1974 to 56.74% in 1989 and to 56.30% in 2002, which can be regarded as stable. In the Qinling mountain system, forested areas changed from 70.05% in 1974 to 66.93% in 1989 and to 67.17% in 2002, which reveals little change in this area. In the Qionglai mountain system, forested areas changed from 72.84% in 1974 to 71.53% in 1989 and to 73.22% in 2002; therefore, an increase can be noted in this area. In the Xiangling mountain system, forested areas also increased from 50.81% in 1974 to 50.20% in 1989 and to 53.15% in 2002. In the Liangshan mountain system, forested areas changed from 68.43% in 1974 to 55.81% in 1989 and to 60.07% in 2002. These results are in accordance with the giant panda population changes in different mountain systems. Thus, the best way to improve the threatened status of the panda population is to protect the current habitat and the potential habitat.     

融合AHP和GIS技术的自然遗产地边界界定方法研究

自然遗产地边界界定对于遗产地保护和管理具有重要的意义。已有的遗产地边界界定方法多基于先验知识和专家经验，具有一定的主观性，精度也有待进一步验证。以卫星遥感影像为基础数据，提出层次分析法（Analytic Hierarchy Process, AHP）和GIS结合的自然遗产地边界界定方法，并将其用于四川卧龙自然保护区边界确定。首先利用AHP分析并构建自然遗产地边界影响因子等级体系，然后基于GIS分析建模，通过数字化得到卧龙自然保护区的边界。与已公开的相关资料相比，该方法划定的保护区面积和边界更加科学、客观、精确，为珍稀动物大熊猫栖息地提供了明确的自然边界，而且突出了人与保护区相互关系及保护区的自然和生态保护价值。     

融合AHP和GIS技术的自然遗产地边界界定方法研究

自然遗产地边界界定对于遗产地保护和管理具有重要的意义。已有的遗产地边界界定方法多基于先验知识和专家经验,具有一定的主观性,精度也有待进一步验证。以卫星遥感影像为基础数据,提出层次分析法（Analytic Hierarchy Process, AHP）和GIS结合的自然遗产地边界界定方法,并将其用于四川卧龙自然保护区边界确定。首先利用AHP分析并构建自然遗产地边界影响因子等级体系,然后基于GIS分析建模,通过数字化得到卧龙自然保护区的边界。与已公开的相关资料相比,该方法划定的保护区面积和边界更加科学、客观、精确,为珍稀动物大熊猫栖息地提供了明确的自然边界,而且突出了人与保护区相互关系及保护区的自然和生态保护价值。     

基于高斯模型的不确定数据流Skyline查询方法

由于数据的动态性及不确定性等特征,使得不确定数据流上Skyline查询研究面临挑战.不确定对象一般采用多元概率密度函数(PDF)表示,现有的不确定数据流Skyline查询方法均采用离散型随机变量建模.然而不确定数据流中的对象可能是连续变化的,离散模型对连续性随机变量难以适用.针对连续PDF建模的不确定数据流Skyline查询进行了研究,提出了基于高斯模型的不确定数据流Skyline查询方法(SGMU),该方法包含2个过程:1)动态高斯建模算法(DGM):对滑动窗口采样并建立高斯模型,将原始的数据流转化为不确定对象PDF的参数流;2)提出了基于高斯树的查询算法(GTS)以建立空间索引结构和执行Skyline查询.实验结果表明,SGMU算法不仅能够对连续型不确定对象进行有效建模以辅助Skyline查询,而且能够有效地减少查询对象个数,提高Skyline查询效率.     

Comparative analysis of MODIS-FAPAR and MERIS-MGVI datasets: Potential impacts on ecosystem modeling

Assessing the ecosystem productivity dynamics is a key issue in the context of the carbon cycle at the southern Europe ecosystems. The availability of remote sensing data and products, as FAPAR, strongly motivates its use in ecosystem modeling. This paper presents a comparative analysis between MODIS-FAPAR and MERIS-MGVI datasets, two products carrying similar information, from regional (the Iberian Peninsula) to local (six biomes) scales. Moreover, an assessment of the impacts of its use in terrestrial ecosystem modeling, by using the CASA production efficiency model is presented. The analysis was conducted for both datasets for the year 2003, and was supported by an extensive statistical analysis, including geostatistics, focusing both seasonality and spatial coherence. Main findings refer to (i) a systematically higher MODIS than MERIS data, both at the Iberian Peninsula and at local biomes, (ii) a seasonality of the correlation between the two datasets, and (iii) a spatial homogeneity over larger extensions from MERIS-MGVI data, opposing to a higher spatial variability from MODIS-FAPAR. Although these different data properties, it was found that the two independent datasets produce reasonable NEP estimates, by using CASA model at the site level. These results suggest that the MODIS-FAPAR and MERIS-MGVI differences can be minimized in biophysical modeling through model re-parameterization, although this process does not solve the seasonal dependence pattern found in the original datasets.     

大熊猫化石鼻窦的3维成像机器虚拟解剖

研究生物系统演化既需要观察标本的外部形态,也需要观察内部形态。由于珍稀化石不允许对标本进行实体解剖,所以珍稀化石的传统形态学研究只能停留在标本的外表。CT技术及三维成像技术的发展已使人们能够将珍稀标本的形态学研究深入到标本内部。现以目前最早、最完整的化石大熊猫头骨为材料,结合现生大熊猫标本,使用CT扫描和三维成像技术获取了大熊猫化石头骨内部的三维影像,从而在无损标本的情况下虚拟提取解剖了头骨内部的副鼻窦,为大熊猫演化的研究提供了新的数据。     

A framework for modeling payments for ecosystem services with agent-based models,Bayesian belief networks and opinion dynamics models

We present an integrated modeling framework for simulating land-use decision making under the influence of payments for ecosystem services. The model combines agent-based modeling (ABM) with Bayesian belief networks (BBNs) and opinion dynamics models (ODM). The model endows agents with the ability to make land-use decisions at the household and plot levels. The decision-making process is captured with the BBNs that were constructed and calibrated with both qualitative and quantitative information, i.e., knowledge gained from group discussions with stakeholders and empirical survey data. To represent interpersonal interactions within social networks, the decision process is further modulated by the opinion dynamics model. The goals of the model are to improve the ability of ABM to emulate land-use decision making and thus provide a better understanding of the potential impacts of payments for ecosystem services on land use and household livelihoods. Our approach provides three important innovations. First, decision making is represented in a causal directed graph. Second, the model provides a natural framework for combining knowledge from experts and stakeholders with quantitative data. Third, the modular architecture and the software implementation can be customized with modest efforts. The model is therefore a flexible, general platform that can be tailored to other studies by mounting the appropriate case-specific “brain” into the agents. The model was calibrated for the Sloping Land Conversion Program (SLCP) in Yunnan, China using data from participatory mapping, focus group interviews, and a survey of 509 farm households in 17 villages.     

Evaluating MODIS data for mapping wildlife habitat distribution

Habitat distribution models have a long history in ecological research. With the development of geospatial information technology, including remote sensing, these models are now applied to an ever-increasing number of species, particularly those located in areas in which it is logistically difficult to collect habitat data in the field. Many habitat studies have used data acquired by multi-spectral sensor systems such as the Landsat Thematic Mapper (TM), due mostly to their availability and relatively high spatial resolution (30 m/pixel). The use of data collected by other sensor systems with lower spatial resolutions but high frequency of acquisitions has largely been neglected, due to the perception that such low spatial resolution data are too coarse for habitat mapping. In this study we compare two models using data from different satellite sensor systems for mapping the spatial distribution of giant panda habitat in Wolong Nature Reserve, China. The first one is a four-category scheme model based on combining forest cover (derived from a digital land cover classification of Landsat TM imagery acquired in June, 2001) with information on elevation and slope (derived from a digital elevation model obtained from topographic maps of the study area). The second model is based on the Ecological Niche Factor Analysis (ENFA) of a time series of weekly composites of WDRVI (Wide Dynamic Range Vegetation Index) images derived from MODIS (Moderate Resolution Imaging Spectroradiometer – 250 m/pixel) for 2001. A series of field plots was established in the reserve during the summer–autumn months of 2001–2003. The locations of the plots with panda feces were used to calibrate the ENFA model and to validate the results of both models. Results showed that the model using the seasonal variability of MODIS-WDRVI had a similar prediction success to that using Landsat TM and digital elevation model data, albeit having a coarser spatial resolution. This suggests that the phenological characterization of the land surface provides an appropriate environmental predictor for giant panda habitat mapping. Therefore, the information contained in remotely sensed data acquired with low spatial resolution but high frequency of acquisitions has considerable potential for mapping the habitat distribution of wildlife species.     

燃烧动力学平台设计

燃料的相关研究在发动机研制中扮演着重要角色,数据是燃烧模型耦合的基础,反应机理是燃烧数值模拟的关键.针对我国在燃烧数据库建设方面的短板问题和个性化燃烧反应机理构建的迫切需求,本文设计了燃烧动力学平台,包括燃烧数据库和燃烧反应机理在线计算系统.燃烧数据库系统通过数据的标准化和分级管理建立了高效的搜索流程,形成了国内独有的...