生猪养殖业污水排放智慧监管系统的设计与实现

为了对生猪养殖业污水的治理过程进行监控和违规排污预警,该文提出了养殖污水实时监管策略,设计并实现了生猪养殖污水治理智慧监管系统。该系统通过信息采集模块收集养殖污水排放的实时数据,实现养殖污水实时数据监测、预警分析等功能。其中集中治理的监管是根据安装在槽罐车上的GPS数据和污水集中处理厂的信息,判断污水是否被运送到指定地点排放;工业治理的监管是采用模糊推理理论,以监管因子的浓度偏差及偏差变化率为输入量,相应的污水预警等级作为输出量,对监管因子进行模糊化及逻辑推理,建立相应的模糊监管子系统,生成工业治理监管规则及策略;针对生态治理的监管,构建了相应的监管策略和Ecological数学模型,该模型以监管策略为依据,对实时数据进行定性与定量分析预测,实现对偷排漏排、满溢等违规排污现象的判断。试验结果表明,系统预警准确度为96.17%,平均误差时间为33.22 s,违规排污量平均值为15.77 L,能够满足养殖污水排放监管要求,对提高监管效率具有重要意义。     

Representing uncertainty in silvicultural decisions: an application of the Dempster–Shafer theory of evidence

Forest management decisions often must be made using sparse data and expert judgment. The representation of this knowledge in traditional approaches to decision analysis implies a precise value for probabilities or, in the case of Bayesian analysis, a precisely specified joint distribution for unknown parameters. The precision of this specification does not depend on the strength or weakness of the evidence on which it is based. This often leads to exaggerated precision in the results of decision analyses, and obscures the importance of imperfect information. Here, I suggest an alternative based on the Dempster–Shafer theory of evidence, which differs from conventional approaches in allowing the allocation of belief to subsets of the possible outcomes, or, in the case of a continuous set of possibilities, to intervals. The Dempster–Shafer theory incorporates Bayesian analysis as a special case; a critical difference lies in the representation of ignorance or uncertainty. I present examples of silvicultural decision-making using belief functions for the case of no data, sparse data, and adaptive management under increasing data availability. An approach based on the Dempster–Shafer principles can yield not only indications of optimal policies, but also valuable information about the level of certainty in decision-making.     

面向林业主题的知识模型构建方法的研究

为了提高面向林业主题的知识系统中知识的数量与质量，在分析知识模型研究现状的基础上，结合林木的生育特点，提出了林木知识模型建立的基本依据是林木生育时间和造林技术措施，并依此建立由浅知识模型和深知识模型所组成的杨树三维知识模型。同时对该知识模型的知识表示和模糊推理进行了描述。该知识模型具有一定的通用性。     

基于事例推理的车身内部布置关键技术

根据汽车车身内部布置的特点,通过扩展对象模型提出了车身内部布置事例的面向对象表达模型,将车身内部布置的约束分为范围、规则和表达式3种形式,并研究了其对象表达模型.利用最近邻方法检索相似事例,结合参数替换和约束满足方法提出了布置事例修改策略,实现了布置事例的修改.利用UG和VC++开发了试验系统,应用实例验证了方法的可行性和有效性.     

基于模糊推理的芒果病虫害诊断系统研究

为了满足实际病虫害诊断对专家系统的需求,根据芒果病虫害特征诊断与防治的特点,结合芒果病虫害的相关信息,基于案例模糊推理的推理机制和结构框架,以VS2008和SQL Server 2000为开发工具,建立了芒果病虫害诊断系统的知识库规则库和案例库,并设计了推理机制,通过专家系统技术和计算机技术构建了面向农户的数字化芒果病虫害诊断系统。系统的实施对于推广植保知识、指导农户科学防治芒果病虫害有一定的现实意义。     

基于Web的犬病诊断专家系统的设计与实现

模拟兽医专家的诊断过程,对290余种犬病用计算机进行诊断模拟,充分发挥Internet收集、共享知识和数据的优势,改善了诊断专家系统的性能并扩展增强了诊断功能.采用正-反向结合的混合推理策略及深度优先的搜索方式,利用MVC设计模式,实现了基于web的犬病诊断专家系统.可以为犬病专家和养犬人士提供方便.     

基于WebGIS的农作物管理地理信息系统的设计与实现

结合农作物目前生产管理的现状及WebGIS技术的发展，利用WebGIS和ES的集成技术、智能推理技术和COM技术，采用B／S的三层架构，以GeoMedia WebMap作为网络地图发布平台，融合农业专家知识和肥料优化分配模型、优化灌溉模型、决策方案评价模型和作物优化布局模型，研制了农作物管理地理信息系统。     

基于信息扩散近似推理的年降水量预测模型

【目的】研究信息扩散近似推理方法在年降水量预测中的应用,并对比其他方法分析其推广前景。【方法】通过分析年降水量时间序列的特性,提出了基于当前趋势以及相邻年份降水量的年降水预测规则。利用信息扩散近似推理描述年降水量间的复杂非线性关系,并以某灌区长系列降水资料为样本进行实例计算。【结果】信息扩散近似推理方法预测效果较好,该方法误差绝对值和为1.673,小于人工神经网络和线性自回归方法的统计结果。【结论】信息扩散近似推理可将样本点转换成模糊集,部分弥补了由于数据的不完备性所造成的信息空白,并可将矛盾模式转换成兼容模式。通过与传统预测方法相比较发现,该模型能够很好地光滑样本数据以及较好地发掘知识,有较高的预测精度和推广应用价值。     

A case-based method of selecting covariates for digital soil mapping

Selecting a proper set of covariates is one of the most important factors that influence the accuracy of digital soil mapping(DSM). The statistical or machine learning methods for selecting DSM covariates are not available for those situations with limited samples. To solve the problem, this paper proposed a case-based method which could formalize the covariate selection knowledge contained in practical DSM applications. The proposed method trained Random Forest(RF) classifiers with DSM cases extracted from the practical DSM applications and then used the trained classifiers to determine whether each one potential covariate should be used in a new DSM application. In this study, we took topographic covariates as examples of covariates and extracted 191 DSM cases from 56 peer-reviewed journal articles to evaluate the performance of the proposed case-based method by Leave-One-Out cross validation. Compared with a novices' commonly-used way of selecting DSM covariates, the proposed case-based method improved more than 30% accuracy according to three quantitative evaluation indices(i.e., recall, precision, and F1-score). The proposed method could be also applied to selecting the proper set of covariates for other similar geographical modeling domains, such as landslide susceptibility mapping, and species distribution modeling.     

基于故障树理论的不精确推理方法研究

推理机是专家系统的重要组成部分,直接影响专家系统的实用性和判断的正确性。文章探讨了农机液压系统故障诊断专家系统的推理机实现问题,研究了基于故障树理论的不精确推理方法,经过系统开发验证,该推理方法应用于故障诊断专家系统中,具有较高的诊断效率和准确性。