LEARNING PRECONDITIONS FOR PLANNING FROM PLAN TRACES AND HTN STRUCTURE

A great challenge in developing planning systems for practical applications is the difficulty of acquiring the domain information needed to guide such systems. This paper describes a way to learn some of that knowledge. More specifically, the following points are discussed. (1) We introduce a theoretical basis for formally defining algorithms that learn preconditions for Hierarchical Task Network (HTN) methods. (2) We describe Candidate Elimination Method Learner ( CaMeL ), a supervised, eager, and incremental learning process for preconditions of HTN methods. We state and prove theorems about CaMeL's soundness, completeness, and convergence properties. (3) We present empirical results about CaMeL's convergence under various conditions. Among other things, CaMeL converges the fastest on the preconditions of the HTN methods that are needed the most often. Thus CaMeL's output can be useful even before it has fully converged.     

THE DISCIPLE–RKF LEARNING AND REASONING AGENT

Over the years we have developed the Disciple theory, methodology, and family of tools for building knowledge-based agents. This approach consists of developing an agent shell that can be taught directly by a subject matter expert in a way that resembles how the expert would teach a human apprentice when solving problems in cooperation. This paper presents the most recent version of the Disciple approach and its implementation in the Disciple–RKF (rapid knowledge formation) system. Disciple–RKF is based on mixed-initiative problem solving , where the expert solves the more creative parts of the problem and the agent solves the more routine ones, integrated teaching and learning , where the agent helps the expert to teach it, by asking relevant questions, and the expert helps the agent to learn, by providing examples, hints, and explanations, and multistrategy learning , where the agent integrates multiple learning strategies, such as learning from examples, learning from explanations, and learning by analogy, to learn from the expert how to solve problems. Disciple–RKF has been applied to build learning and reasoning agents for military center of gravity analysis, which are used in several courses at the US Army War College.     

GH36合金中M23C6、VC的界面结构

使用透射电镜观察了GH36合金中M₂₃C₆、VC碳化物和基体之间的界面结构,在M₂₃C₆/γ的半共格界面上分布有错配位错列,间距约10nm。根据${\\rm{\\vec g}}$·${\\rm{\\vec b}}$=0判据,可以确定该位错的Burgers vector ${\\rm{\\vec b}}$=a/2(110)γ,VC/γ的相界面是具有很大点陈错配度(δ=+0.155)的共格界面。最后,从M₂₃C₆、VC和基体之间的不同界面结构与性质讨论了以碳化物沉淀为主要强化手段的Fe基GH36合金的强韧化本质以及如何识别波纹图(Moire pattern)和界面位错的问题。     

基于CIM/G的图形多版本管理与展示

针对智能电网调度控制系统在基于时间维度的图形管理与展示方面的不足,提出一种基于CIM/G的图形多版本管理与展示技术,使用CIM/G文件作为持久化存储介质,根据模型版本划分相应的图形版本标识,实现变化图形版本存储和图形多版本安全隔离,进而实现图形多版本的构建管理技术方案,同时给用户提供图形任意版本快速浏览、图形多版本差异集中展示的方法。该项技术已在华北电力调控分中心试点应用,可满足多项业务应用对多时态版本图形的需求,提升了调控系统对图形版本构建、存储、展示等方面的管控能力。     

激光视觉融合下的运动检测与失配矫正

针对单一传感器在动态场景感知问题上的局限性,设计了一种融合激光与视觉的实现系统,并对运动检测中的背景显露区误判问题和融合中不同传感器间点云的失配问题分别提出了改进算法。在运动检测上,首先基于视觉的背景差分算法对激光进行前景点分拣,再以激光前景点为启发信息进行视觉前景聚类。在融合失配问题上,首先基于栅格失配度分别对激光和视觉点云进行聚类分割,再以激光为基准,逐一将对应的视觉点云与之配准,滤除噪声后所得到的矫正点云可用于场景重建进行进一步验证。实验结果表明,改进算法所获得的融合前景对\"影子\"有更好的鲁棒性;较之整体配准的矫正,改进算法在平均失配度上降低了约75%,在y和z方向上的偏移比收敛了至少5%。     

质量管理也要与时俱进--换版认证前先推行新标准必要性刍议

新颁布的ISO9000：2000标准，融资了世界各国质量管理的理论研究成果与实践经验，为企业进一步提高质量管理水平和市场竞争力，提供了新机遇，已获得94版ISO9000标准审核认证证书的企业，尽管证书有效期未到，但也应及时学习贯彻推行新的标准。     

面向科学计算的复杂产品工程设计数据管理研究

复杂产品设计具有反复性、试探性、选择性等特点,其间产生的大量关系复杂、类型各异的科学数据的组织与管理已成为影响设计性能的一个瓶颈问题.对此,在分析归纳基于科学计算的工程设计特点的基础上,引入了描述设计对象属性及相关业务处理逻辑的业务构件概念,采用了"关系数据库 原始文件 XML Schema"的数据存储方式,提出了基于构件和项目版本的网状版本关系模型,以实现对过程数据和阶段数据进行分别管理,给出了版本合并及可视化等关键技术的实现方式.最后,以航空发动机涡轮设计为例进行了验证.     

中小型金属工件在线检测系统的设计

随着机器视觉技术的快速发展,利用图像处理技术对工件进行在线实时检测的方法近年来得到广泛的应用和发展。为了解决工件离线人工检测效率低、精度差的问题,设计了包括上料模块、图像采集处理模块、零件传送模块、筛选模块和控制模块的基于机器视觉的工件在线检测剔除系统。系统运用PLC可编程逻辑控制器作为工业控制器,机器视觉和图像处理作为检测方法,提高了工件检测的产品品质、生产效率和自动化程度,该机器视觉检测系统可以通过改变程序的方法,运用和推广到其他表面品质检测的行业中。