基于Rough Set理论的油层识别方法

本文简介了智能信息处理中新出现的Ｒｏｕｇｈ Ｓｅｔ（ＲＳ）理论,讨论了ＲＳ理论进行决策分析的方法,提出了将连续属性离用化的最优化思想,并探讨了ＲＳ理论用于测井数据判别油水层的问题。判别结果表明;本方法具有速度快,易实现,可优选属性等特点,且判别符合油水层的问题,判别结果表明：本方法具有速度快,易实现,可优选属性等特点,且判别符合率优于手工方法与ＢＰ网络方法,具有实用价值。     

土坝的震害预测——人工神经元网络方法的应用

作者前文曾对土坝震害给出速评分析结果,但回判标准差仍嫌偏大。本文对资料进行了筛选,去掉烈度为Ｖ度和Ｘ度的部分震例,震例数由１８０个减少到１８６个。考虑到震害与影响震害的诸因素间可能存在高度非线性关系,本文采用人工神经元网络方法对土坝的震害进行预测。结果表明,回判标准差由０．７０８降低为０．４８６,即降低了３１．４％。     

爆破振动对铁厂沟村房屋选择性破坏的调查与分析

乌鲁木齐矿务局露天矿的剥离爆破对距爆点１ｋｍ外的铁厂沟材房屋产生了选择性破坏,振动对个体房屋的破坏呈现出鲜明的选择性,不同房屋遭受的破坏程度差异极大。     

远程智能数据采集与处理系统设计

分析了数据采集的基本方法，重点论述了远程智能数据采集系统的设计思想，方法和技术，实现了多点数据采集与处理系统的设计。     

敦煌石窟虚拟重现与壁画修复模拟

由于受到自然灾害、经济建设、旅游开发等因素的影响，许多珍贵稀有文物遗址已处于濒危境地，如何有效保护、研究与开发这些文物资源已经显得非常的迫切与重要。研究了如何将计算机技术与传统文物保护工作结合起来，对文物实施数字化保存、重现和修复；并以敦煌文物为研究对象，利用虚拟现实、图像处理与人工智能等技术实现了敦煌石窟虚拟展示、脱落壁画复原与演变模拟等系统，极大地提高和改善了文物保护研究的效率与效果。     

使用人工神经网络进行我国大陆强震时间序列预测

使用人工神经网络对中国大陆最大地震时间序列进行预测，预测次年的中国大陆最大地震震级，结果表明该方法具有较好的预报效果。还表明中国大陆强震活动除了与强震时间序列本身有关外，还与全球的强震活动，太阳黑子活动等有密切的非线性关系。     

Artificially intelligent geostatistics: A framework accommodating qualitative knowledge-information

The purpose of this paper is to stress the need to examine Al-based models and techniques in dealing with qualitative knowledge, information, and expertise in geostatistics. A model of artificially intelligent geostatistics is proposed as a general framework. The model focuses on the Geostatistician, an abstraction of the collective knowledge and intelligence of the geostatisticians, be they theoreticians and/or practitioners. Dynamic aspects of the model are examined in the context of an explicit knowledge formalism, integrating geostatistical knowledge, symbolic non-algorithmic techniques for knowledge-information representation and inference, and standard numerical data processing. Two implementations of related computer systems are given together with case studies.     

An application of artificial intelligence for rainfall-runoff modeling

This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R ²) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.     

Case-based system for slope design

A key part of slope design is the review of past examples of slopes in similar ground conditions. This paper details the development of the SlopeSafe computer program which uses case-based reasoning to formalise this process. The program, written in Visual Basic, draws on a case-base of nearly 3000 case histories of successful and failed slopes to give an indication of the likely success of a proposed slope by matching its geometry and ground conditions to the slopes held in the case-base. XML (Extensible Markup Language) has been used to store the data and a specific set of tags has been defined to provide a standard way of storing slope information. The system has been identified by practising engineers as having the potential to be a very useful design tool.     

Neural networks and landslide susceptibility: a case study of the urban area of Potenza

For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful information regarding possible future phenomena. Such maps represent a fundamental step in the drawing up of adequate measures of landslide hazard mitigation. However, for a complete estimation of landslide hazard, meant as the degree of probability that a landslide occurs in a given area, within a given space of time, detailed and uniformly distributed data regarding their incidence and causes are required. This information, while obtainable through laborious historical research, is usually partial, incomplete and uneven, and hence, unsatisfactory for zoning on a regional scale. In order to carry this out effectively, the utilization of spatial estimation of the relative levels of landslide hazard in the various areas was considered opportune. These areas were classified according to their levels of proneness to landslide activity without taking recurrence periods into account. Various techniques were developed in order to obtain upheaval numerical estimates. The method used in this study, which was applied in the area of Potenza, is based on techniques derived from artificial intelligence (Artificial Neural Network—ANN). This method requires the definition of appropriate thematic layers, which parameterize the area under study. These are recognized by means of specific analyses in a functional relationship to the event itself. The parameters adopted are: slope gradient, slope aspect, topographical index, topographical shape, elevation, land use and lithology.