不确定拓扑关系模糊推理

针对定性空间推理难以处理不确定空间拓扑关系推理的缺憾,将确定拓扑关系复合表推理方法扩展到模糊拓扑空间,建立了基于拓扑关系复合表的不确定拓扑关系模糊推理模型,设计了基于RCC5的拓扑关系复合表的模糊推理规则库和推理的方法流程,并结合具体计算实例对模糊推理模型进行了实验研究,推理结果验证了模型的认知合理性和可行性。     

地理空间中的空间关系表达和推理

针对地理空间中的应用,归纳了在空间关系的表达与推理中不同于人工智能领域研究的一些特点:在人工智能领域,更注重建立形式化的推理系统;而在地理信息科学中,则需更关注地理空间的特点以及地物的地理语义。该文基于地理空间和地理现象的本质且顾及地理空间认知,总结了地理空间中空间关系表达和推理的特点,具体包括空间的有限性、地球的球面特征、地物的地理语义、地物形状的复杂性、面状地物、特殊的空间关系、空间关系的层次性与尺度相应原则、不确定性、三维与时态特性九方面;进而介绍了地理空间关系表达的两个应用,即地理信息检索和基于对象的图像分析。该文的探讨可为地理信息科学中的相关研究提供方向性指导。     

3维GIS中位置关系的定性描述与推理

3维空间中位置关系的定性描述和推理是当前城市和矿山GIS界的重要理论课题之一,主要涉及如何用定性距离和方向关系来综合描述目标对象及相对于其参照物的空间位置关系的方法。本文基于投影系统划分空间方向区域,利用解析几何工具表达距离关系和方向关系,建立组合距离和方向关系来描述位置关系的概念框架,导出位置关系组合推理的一般表达式,并通过例子说明一些典型的位置关系定性推理过程和结果。     

Indexing large geographic datasets with compact qualitative representation

This paper develops a new mechanism to efficiently compute and compactly store qualitative spatial relations between spatial objects, focusing on topological and directional relations for large datasets of region objects. The central idea is to use minimum bounding rectangles (MBRs) to approximately represent region objects with arbitrary shape and complexity and only store spatial relations that cannot be unambiguously inferred from the relations of corresponding MBRs. We demonstrate, both in theory and practice, that our approach requires considerably less construction time and storage space, and can answer queries more efficiently than the state-of-the-art methods.     

基于典型事例推理的路径规划方法研究

提出了把基于典型事例推理的智能方法应用到路径规划过程中。在结合道路网络知识的基础上,定义了典型事例,并基于事例进行了推理。实验结果表明,该算法减少了搜索空间,加快了搜索速度,并满足人们喜欢在熟悉路径上行驶的偏好。     

The world,the computer,and the mind: how Andrew Frank helped make human language and cognition cornerstones of geographic information science

During the late 1980s and early 1990s, cognitive science was included as one of the key disciplines in the emerging multidisciplinary field of geographic information science (GIScience). One of the key proponents and popularizers of the study of human cognition as part of GIScience – and one of its major researchers – has been Andrew U. Frank. In this essay, we review the history of Andrew Frank’s role as an innovator and champion for cognitive GIScience, and summarize some of his research contributions in this domain. Taken along with his contributions to other areas of GIScience, this review shows that Andrew Frank has been one of the primary figures in modern GIScience, and among its very broadest and intellectually-diverse contributors.     

A New Method to Predict Erythrocyte Sedimentation Rate with Natural Geographical Factors and Location by Case-based Reasoning: A Case Study of China

Reference values of erythrocyte sedimentation rate(ESR)are the key to interpret ESR blood test in clinic.The common local reference ESR values are more accuracy in blood test that are established with natural geographical factors by using the multiple linear regression(MLR)model and the artificial neural network(ANN).These knowledge-based methods have limitations since the knowledge domains of ESR and natural geographical factors are limited.This paper presents a new cases-depended model to establish reference ESR values with natural geographical factors and location using case-based reasoning(CBR)since knowledge domain of ESR and geographical factors is weak.Overall 224 local normal ESR values of China that calculated from 13623 samples were obtained,and the corresponding natural geographical factors and location that include altitude,sunshine hours,relative humidity,temperature,precipitation,annual temperature range and annual average wind speed were obtained from the National Geomatics Center of China.CBR was used to predict the unseen local reference ESR values with cases.The average absolute deviation(AAD),mean square error(MSE),prediction accuracy(PA),and Pearson correlation coefficient(r)between the observed and estimated data of proposed model is 33.07%,9.02,66.93% and 0.78,which are better than those of ANN and MLR model.The results show that the proposed model provides higher prediction accuracy than those of the artificial neural network and multiple linear regression models.The predicted values are very close to the observed values.Model results show significant agreement of cases data.Consequently,the model is used to predict the unseen local reference ESR with natural geographical factors and location.In spatial,the highest ESR reference areas are distributed in the southern-western district of China that includes Sichuan,Chongqing,Guangxi and Guizhou provinces,and the reference ESR values are greater than 23 mm/60 min.The higher ESR reference values are distributed in the middle part and northern-eastern of China which include Hubei,Henan,Shaanxi,Shanxi,Jilin and Heilongjiang provinces,and the reference ESR values are greater than 18 mm/60min.The lowest ESR reference values are distributed in the northern-western of China that includes Tibet and Xinjiang,and the reference ESR values are lower than 5 mm/60min.     

基于云推理的年降水量预测

预测未来年的降水量是对旱情做出预警并采取合理抗旱措施的重要前提,但如何获得准确的预测值一直是研究的热点。降水随时间的变化通常具有随机性和模糊性,而云模型是在传统模糊数学和概率统计的基础上建立起来的,能够实现不确定概念与定量数值之间的自然转化,通过年降水量历史数据及当前趋势挖掘并制定出相应规则,从而推理获得未来年份的降水量。在此基础上,提出了基于小波消噪预处理和理论频率曲线修正的云推理预测模型,并将其运用到了西安市年降水量的预测当中。从所得结果来看,降水量预测值能较好的反映其年际变化规律,模型预测精度得到了很大提高,基本可将误差控制在30%以内。     

An evidential reasoning-based sustainability index for water resources management

ABSTRACT

Evidential reasoning (ER) is introduced as a new basis for developing and implementing sustainability indices (SI). The ER-based evaluations keep the merits of previous well-known sustainability indices and provide added values such as more accurate grade-based assessment and aggregation of performance criteria. The proposed modifications significantly improve the capability of the SI to scrutinize and differentiate various water-supply conditions. Available data from Gorganrud-Qaresou basin, northern Iran, are used to evaluate the applicability and efficiency of the sustainability index. The results of the proposed method are compared with two water resources sustainability indices with similar concepts but different bases. It is shown that the ER-based SI can better distinguish undesirable water-supply scenarios.