专家系统在化工中的应用进展

本文综述了近年来专家系统在化工领域的应用状况，并指出今后的发展趋势。     

AI paradigms

The aim of this paper is comparative analysis of most important AI paradigms. An AI paradigm is defined as the pair composed by a concept of intelligence and a methodology in which intelligent computer systems are developed and operated. Three paradigms, the behaviourist paradigm, the agent paradigm, and the artificial life paradigm are discussed.     

基于Sql Server BI平台和决策树技术的CET-4成绩分析

文章首先对Sql Server 2008商业智能平台及决策树技术相关理论作了介绍,然后对挖掘数据源数据进行了一系列预处理。利用Sql Server 2008商业智能平台下的决策树技术并采用数据挖掘扩展语言即DMX语言创建了CET-4成绩分析决策树模型。分类矩阵和挖掘提升图分别对该模型的评估结果表明了模型具有较高的可靠性和分类准确度,同时模型对应的一些规则可作为英语教学管理和改革的重要参考依据。     

主动推送模式下的情报需求预测模型

针对主动推送模式下的情报需求预测问题,提出了基于案例推理的解决方案。设计了进行情报需求预测案例分析的工作流程,建立了案例属性描述模型;应用最近邻法进行案例的相似度评价,并通过信息增益的计算确定每个属性的权值,获得相似案例(集);提出了方案调整和推理策略。     

Binary particle swarm optimization with multiple evolutionary strategies

This paper introduces a novel variation of binary particle swarm optimization(BPSO) algorithm and a further extension to improve its performance.Firstly,mimicking the behaviors of some creatures group,multiple evolutionary strategies BPSO(MBPSO) is introduced which takes different evolutionary strategies for various particles according to their performances.Then,on the basis of MBPSO,a new strategy is discussed to improve the performance of the MBPSO(M2BPSO) which adopts the concept of the mutation operator aiming to overcome the premature convergence and slow convergent speed during the later stages of the optimization.The proposed two algorithms are tested on seven benchmark functions and their results are compared with those obtained by other methods.Experimental results show that our methods outperform the other algorithms.     

潜油电泵智能选型系统的设计与实现

潜油电泵的智能选型对浅海油田的石油开采具有现实意义。在智能选型系统中建立相关的计算模型并完成电机表面流速、挂泵深度、井底流压等多项参数的计算。利用计算机进行数值计算、绘图及数据存储,将石油勘探、钻井、完井、采油过程中产生的数据有机地结合在一起,构建利用计算机平台处理的潜油电泵机组智能选型设计模型。从而提高工作效率、节省大量的资金,使浅海油田的石油开采从传统方式向智能化迈进一步。     

智慧水利解析

智慧水利是水利高质量发展的显著标志,是运用大数据、人工智能、物联网、云计算、移动互联等新一代信息通信技术,基于自然水系、水利工程体系和水利管理体系,智慧地将适量适质的水适时送到适地,为实现长久水安澜、优质水资源、宜居水环境、健康水生态提供支撑。智慧水利包括目标、对象、时空、技术、价值、成效等六个维度的内涵。目标维度包括问题识别与目标制订,对象维度包括服务主体与水利业务,时空维度包括监控环节与决策过程,技术维度包括赋能体系与核心技术,价值维度包括概念模型与推进路径,成效维度包括功能表征与建设愿景。在智慧水利建设中,数据是基础,要高度重视水利数据的收集、开放和共享;信息是关键,要高度重视监测数据的信息提取和分析;知识是核心,要加强水利知识的挖掘、提升和知识的积累;智能和智慧是目的,要重视人工智能的使用和标准化管理体系的构建。     

洪水预报智能模型在中国半干旱半湿润区的应用对比

为探究不同洪水预报智能模型在我国半干旱半湿润区的应用效果,选用决策树、多层感知器、随机森林和支持向量机4种模型在陕西省3个典型流域进行逐时洪水预报;选择相关系数、纳什效率系数、均方根误差、平均绝对误差和相对误差等评价指标,比较不同预见期下4种模型在半干旱半湿润典型流域洪水预报的适用性。结果表明:在短预见期预报中,4种模型在半湿润区典型流域均可获得较高的预报结果,在半干旱典型流域模拟精度相对偏低,仅支持向量机模型满足预报要求;随着预见期延长,不同模型性能变化差异大,支持向量机模型整体稳定,在小流域实时洪水预报中具有明显优势;随机森林模型与决策树模型精度随预见期延长而缓慢下降,前者适应性更好;多层感知器模型精度随预见期延长而骤减,模型稳定性较差。     

Semi-automated user agents in distributed change management

Flexible integration of distributed design and manufacturing activities is one of the key issues in applying just-in-time principles along the logistics chain. The potential of AI based approaches in this field is clear, but the inherent asynchrony, concurrency, and inconsistency of distributed operations has reduced enthusiasm so far.This paper discusses coordination in the context of geographically distributed manufacturing of one-of-a-kind products. Cooperation of partners is based on semi-automated user agents which operate as an integrative layer between more dedicated tools and systems. The management of order changes in elevator manufacture is used as a case example.The work reported here is part of the DIMUN project (DIMUN, 1989) in the RACE program.     

What can the history of AI learn from the history of science?

There have been few attempts, so far, to document the history of artificial intelligence. It is argued that the historical sociology of scientific knowledge can provide a broad historiographical approach for the history of AI, particularly as it has proved fruitful within the history of science in recent years. The article shows how the sociology of knowledge can inform and enrich four types of project within the history of AI; organizational history; AI viewed as technology; AI viewed as cognitive science and historical biography. In the latter area the historical treatments of Darwin and Turing are compared to warn against the pitfalls of rational reconstructions of the past.