江苏沿江各市工业绿色化程度的模糊评价

工业绿色化即工业企业逐步减少污染排放,实现环境无害化的过程,这一过程的进展情况直接影响到区域可持续发展水平。从工业绿色化意识、工业绿色化管理、工业绿色化绩效3个方面构建了区域工业绿色化发展的评价指标体系。为了使评价结果更为科学合理,还对江苏沿江8市的244家工业企业进行问卷调查,在此基础上,结合各市单位产值能耗及污染排放强度、水循环利用率、废水达标排放率、废物综合利用率等工业绿色化绩效的实际值建立多级模糊综合评价模型,对江苏省沿江8市的工业绿色化发展水平进行评价。结果发现,苏州、无锡等苏南经济较发达地区的工业绿色化水平较高,而扬州、镇江、泰州等苏中地区的工业绿色化水平较低。最后提出了加快工业绿色化发展,缩小地区间差距的具体建议     

含模糊随机变量的水污染控制系统研究

在水流量为模糊随机变量且河流中工业污水含量标准给定的条件下,以极小化污水处理费用为目标,建立了水污染控制系统问题的模糊随机机会约束规划模型。采用将模糊随机模拟、神经元网络及遗传算法相结合的混合遗传算法来求解该模型,并用算例进行了验证,结果表明该算法是有效可行的。     

阜新市生态地质环境质量模糊综合评价

生态地质环境质量评价,实为生态地质环境的适宜性评价。本文以地貌类型、森林覆盖率、人口密度等16个指标为主要影响因子,确定了生态地质环境质量评价指标的单因素评价模型和评价指标体系。采用模糊数学方法,对阜新市生态地质环境质量进行了综合评价。结果表明阜新地区生态地质环境质量总体良好。经验证,定量评价与定性评价结果较吻合,证明该套基于模糊综合评判技术的生态地质环境质量的定量评价是切实可行的．结论是可信的。     

房地产建设项目生态宜居性的模糊综合评价

以现代住区人居生态环境的要求和前人研究为基础,立足于以人为本和便于实际评估工作的开展,综合考虑人居生态环境健康、舒适的特点,在类比众多评价方法和参考国内外指标体系的基础上,运用模糊综合评价对某一大型房地产项目的生态宜居性进行初步分析,以期完善、充实中国房地产项目的生态宜居性评价体系和方法,为该领域的进一步研究提供基础。     

中国省际水风险评价

选取中国31个省(市、自治区)为研究对象,建立了以行政区域为研究尺度的水风险评价指标体系。运用决策分析模型,通过层次分析法与熵值法确定满足主客观条件的综合权重,在此基础上构建模糊识别模型,对2000—2013年中国31个省(市、自治区)水风险水平进行了评估,并从时间和空间两个维度进行分析。结果表明:1)在研究期间,中国水风险水平不断下降,大部分地区从高值、中高值向中值及中低值靠拢;2)3个子系统中,胁迫性风险一直处于上升状态,中国水资源压力较大并有极大可能继续上升,在现有技术条件下,水风险形势严峻;敏感性风险在3个子系统中贡献较低,各地区抵抗风险能力逐步提高;适应性风险稳步下降,适应风险能力提高,但下降速度减缓,表明治理水问题的难度增大;3)各地区风险来源从单一性演变成复合型,风险来源不同且空间分布不均,治理中国水风险需针对各省(市、自治区)风险来源制定针对性政策。     

模糊灰色聚类和组合赋权法在铁路信号系统可靠性评估中的应用

针对模糊综合评判法在进行列车运行控制系统可靠性评估时的不足,提出基于模糊灰色聚类和组合赋权法的可靠性评估方法,以更客观地评估铁路信号系统的可靠性,降低评估结果的不准确性和不可靠性。首先,利用组合赋权法分别计算CTCS-3级列车运行控制系统各子系统基本单元的权重。然后,采用灰色聚类法评估列车运行控制系统各子系统的可靠性。最后,在对列车运行控制系统子系统可靠性评估结果的基础上,结合各子系统权重值,利用模糊综合评判方法,评估整个列车运行控制系统的可靠性。结果表明,基于模糊灰色聚类和组合赋权法的可靠性评估方法能够实现CTCS-3级列车运行控制系统可靠性评估,评估结果符合实际情况。     

船舶混合锂离子电池储能系统功率分配研究

目的提高极端海况下船舶的适航性和动力性。方法针对能量型和功率型锂离子电池不同工作特性,设计半主动式船舶混合锂离子电池储能系统拓扑结构,提出基于模糊控制理论的功率分配策略,并根据锂离子电池荷电状态(StateofCharge,SOC),限制其最大充放电电流。结果提出的控制策略可充分发挥钴酸锂电池的功率特性和磷酸铁锂电池的能量特性,直流母线电压波纹小于2%。结论在极端海况下,船舶混合锂离子电池储能系统可减少磷酸铁锂电池放电循环次数和放电深度,延长磷酸铁锂电池组的使用寿命,平抑脉冲负载扰动,提高船舶电网稳定性。     

GIS and geostatistics: Essential partners for spatial analysis

Initially, geographical information systems (GIS) concentrated on two issues: automated map making, and facilitating the comparison of data on thematic maps. The first required high quality graphics, vector data models and powerful data bases, the second is based on grid cells that can be manipulated by suites of mathematical operators collectively termed map algebra. Both kinds of GIS are widely available and are taught in many universities and technical colleges. After more than 20 years of development, most standard GIS provide both kinds of functionality and good quality graphic display, but until recently they have not included the methods of statistics and geostatistics as tools for spatial analysis. Recently, standard statistical packages have been linked to GIS for both exploratory data analysis and statistical analysis and hypothesis testing. Standard statistical packages include methods for the analysis of random samples of cases or objects that are not necessarily co-located in space—if the results of statistical analysis display a spatial pattern then that is because the underlying data also share that pattern. Geostatistics addresses the need to make predictions of sampled attributes (i.e., maps) at unsampled locations from sparse, often expensive data. To make up for lack of hard data geostatistics has concentrated on the development of powerful methods based on stochastic theory. Though there have been recent moves to incorporate ancillary data in geostatistical analyses, insufficient attention has been paid to using modern methods of data display for the visualization of results. GIS can serve geostatistics by aiding geo-registration of data, facilitating spatial exploratory data analysis, providing a spatial context for interpolation and conditional simulation, as well as providing easy-to-use and effective tools for data display and visualization. The value of geostatistics for GIS lies in the provision of reliable interpolation methods with known errors, methods of upscaling and generalization, and for supplying multiple realizations of spatial patterns that can be used in environmental modeling. These stochastic methods are improving understanding of how errors in models of spatial processes accrue from errors in data or incompleteness in the structure of the models. New developments in GIS, based on ideas taken from map algebra, cellular automata and image analysis are providing high level programming languages for modeling dynamic processes such as erosion or the development of alluvial fans and deltas. Research has demonstrated that these models need stochastic inputs to yield realistic results. Non-stochastic tools such as fuzzy subsets have been shown to be useful for spatial analysis when probabilistic approaches are inappropriate or impossible. The conclusion is that in spite of differences in history and approach, the linkage of GIS, statistics and geostatistics provides a powerful, and complementary suite of tools for spatial analysis in the agricultural, earth and environmental sciences.     

基于模糊层次分析法的警用非致命空气炮作战效能评估

目的评估警用非致命空气炮的作战效能。方法首先运用层次分析法,确立警用非致命空气炮作战效能评估指标体系,构建作战效能的模糊层次综合评判模型,并采用加权平均算法和平均随机一致性指标,进行各因素权重计算和一致性检验,最后通过模糊综合评判,评估得出警用非致命空气炮作战效能。结果警用非致命空气炮综合评估得分为82.92分,其性能评估为"较好"。结论通过评估结果,反映出警用非致命空气炮综合作战效能较好,具有推广价值和应用前景,为空气炮优化设计和实战应用提供了参照。     

模糊关联分析法及其应用

以环境污染指数为基础,提出了综合集成方法--模糊关联分析法(FRA),并将其应用于大气环境质量评价之中.该方法融合了模糊综合评判法(FCE)和灰色关联法(GR)的优点,能够处理模糊性和不确定性信息,在亦此亦彼的现象中寻求差异,判断出在上一层次中亦此亦彼的信息,在下一层次可能是非此即彼.通过实例分析和与其它方法的比较,该方法评价结果客观、合理.