基于多源类比的MIS表格生成

针对开发信息管理系统时设计者都要花很多时间来设计ＭＩＳ的输出表格，而同时许多ＭＩＳ表格却有很大的相似性的情况，文中提出了多源类比的拆分和重组的理论及如何利用个理论进行自动ＭＩＳ表格生成。还具体阐述了表格的矩阵化表达和利用三层多叉树进行结构类比生成的方法。     

管理信息系统自动生成集成环境autoMIS

本文在分析现有ＭＩＳ软件开发工具和开发环境的基础上，介绍了一个面向ＭＩＳ的应用系统自动生成集成开发－ａｕｔｏＭＩＳ的设计思想、总体框图、功能概述以及部分实现技术。ａｕｔｏＭＩＳ从数据库管理本身的特点和ＭＩＳ的共同点及动态性出发，进行了抽象和总结。使ａｕｔｏＭＩＳ与具体的ＭＩＳ无关，具有普遍的通用性。     

MIS结构控制设计法

本文介绍了应用ＭＩＳ结构控制器开发ＭＩＳ的设计思想与方法。利用结构控制器控制ＭＩＳ的系统结构，可以缩短ＭＩＳ开发周期，提高系统的自动生成程度，增强系统的可扩充性和可读性。该方法对各个领域的ＭＩＳ开发都适用，具有一定的实用和推广价值。     

多媒体MIS自动生成工具模型

传统的ＭＩＳ开发费时费力，无法解决日益突出的软件供求矛盾。解决问题的途径之一是提供ＭＩＳ自动生成工具。本文总结了用数据库来生成、管理ＭＩＳ的基本设计思想，并讨论了用户输入方式，文件格式，还给出针对多媒体表现方式多样的基本解决方法，讨论了可视化手段和面向对象的思想，从整体上构筑成一个多媒体ＭＩＳ自动生成工具模型。     

面向对象的管理信息系统分析与设计

本文对面向对象方法（ＯＯ）应用于ＭＩＳ系统开发中的诸多问题进行了较详尽的讨论，提出了ＯＯ对象在管理应用领域存在管理生命周期（ＭＬＣ）的新思想，并将该思想应用到面向对象的ＭＩＳ系统分析与设计中，取得了较好的效果。     

论面向CIMS管理与决策软件的UIMS设计方法

用户界面管理系统ＵＩＭＳ（ＵｓｅｒＩｎｔｅｒｆａｃｅＭａｎａｇｅｍｅｎｔＳｙｓｔｅｍ）是为用户界面设计者提供良好设计环境的辅助设计工具。本文首先讨论了人机交互软件的特殊性并从方法学的角度出发，研究其开发过程，提出了一种人机交互软件系统的开发方法－ＨＣＳＤＭ（Ｈｕｍａｎ－Ｃｏｍ－ｐｕｔｅｒＩｎｔｅｒａｃｔｉｏｎＳｙｓｔｅｍＤｅｖｅｌｏｐｍｅｎｔＭｅｔｈｏｄ）。其次，从ＵＩＭＳ的基本特征、ＵＩＭＳ模型和ＵＩＭＳ规范说明出发，讨论了在ＵＩＭＳ研究与设计中应考虑的问题。最后，对ＵＩＭＳ的研究与开发前景进行了一些展望     

反应块及其在类比推理中的应用

本文讨论了类比在问题求解中的应用问题．给出了应用反应块识别类比源以及自动生成反应块的算法．本文还给出了一种解法序列分割方法，用于类比源的获取及存储．这些思想和方法已在符号积分求解与学习系统ＩＳＬＳ（ｉｎｔｅｇｒａｔｉｏｎｓｏｌｖｉｎｇａｎｄｌｅａｒｎｉｎｇｓｙｓｔｅｍ）中实现．     

基于软件重用的MIS应用系统自动生成

本文通过对ＭＩＳ的共性分析，采用软件重用技术，以与数据分离了的程序即本文所称的ＭＩＳ工具做为底层基础，用二次生成技术，较好地解决了ＭＩＳ应用系统自动生成的问题，并较好地利用了以往ＭＩＳ开发中的大量宝贵软件资源，程序实现名为ＩＤＥＭＩＳ和ＭＩＳ应用系统集成开发环境，用Ｃ语言实现。     

运用IDEF方法进行CIMS设计

本文主要讨论运用ＩＤＥＦ方法进行ＣＩＭＳ设计的问题，详细介绍了在ＣＩＭＳ的系统分析设计阶段采用ＩＤＥＦ０方法建立系统功能模型的基本过程和系统功能的分解原则，     

基于应用平台的MIS开发与实践

在新技术推动下，ＭＩＳ的建设发生了巨大变化，传统的ＭＩＳ开发理论已不能提供有效的支持，构造ＭＩＳ应用平台ＭＡＰ体系构架不失为ＭＩＳ建设的一个新的台阶。本文论述了ＭＡＰ体系的支持构架和技术群的组成，以及ＭＡＰ体系的特征，并通过实例在７个层次上对ＭＡＰ的构成及设计思想进行了探讨和研究。     

The Variable Precision Rough Set Inductive Logic Programming Model and Strings

The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). The generic Rough Set Inductive Logic Programming (gRS-ILP) model provides a framework for ILP when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. The gRS-ILP model is extended in this paper to the VPRSILP model by including features of the VPRS model. The VPRSILP model is applied to strings and an illustrative experiment on transmembrane domains in amino acid sequences is presented.     

感应传输温盐深链水上低功耗控制系统设计

感应耦合数据传输温盐深（ CTD）链是采用感应耦合技术实时获取CTD链上多台水下CTD传感器采集数据的仪器。以MSP430为主控制器,设计低功耗的水上控制系统,实现了控制水下传感器的采集、获取并存储水下传感器数据、与浮标上位机通信等功能。测试表明：该水上控制系统运行平稳,且在降低功耗方面有很好的性能。     

Inductive logic programming for relational knowledge discovery

Inductive logic programming (ILP) is concerned with the induction of logic programs from examples and background knowledge. In ILP, the shift of attention from program synthesis to knowledge discovery resulted in advanced techniques that are practically applicable for discovering knowledge in relational databases. This paper gives a brief introduction to ILP, presents selected ILP techniques for relational knowledge discovery and reviews selected ILP applications. Nada Lavrač, Ph.D.: She is a senior research associate at the Department of Intelligent Systems, J. Stefan Institute, Ljubljana, Slovenia (since 1978) and a visiting professor at the Klagenfurt University, Austria (since 1987). Her main research interest is in machine learning, in particular inductive logic programming and intelligent data analysis in medicine. She received a BSc in Technical Mathematics and MSc in Computer Science from Ljubljana University, and a PhD in Technical Sciences from Maribor University, Slovenia. She is coauthor of KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems, The MIT Press 1989, and Inductive Logic Programming: Techniques and Applications, Ellis Horwood 1994, and coeditor of Intelligent Data Analysis in Medicine and Pharmacology, Kluwer 1997. She was the coordinator of the European Scientific Network in Inductive Logic Programming ILPNET (1993–1996) and program cochair of the 8th European Machine Learning Conference ECML’95, and 7th International Workshop on Inductive Logic Programming ILP’97. Sašo Džeroski, Ph.D.: He is a research associate at the Department of Intelligent Systems, J. Stefan Institute, Ljubljana, Slovenia (since 1989). He has held visiting researcher positions at the Turing Institute, Glasgow (UK), Katholieke Universiteit Leuven (Belgium), German National Research Center for Computer Science (GMD), Sankt Augustin (Germany) and the Foundation for Research and Technology-Hellas (FORTH), Heraklion (Greece). His research interest is in machine learning and knowledge discovery in databases, in particular inductive logic programming and its applications and knowledge discovery in environmental databases. He is co-author of Inductive Logic Programming: Techniques and Applications, Ellis Horwood 1994. He is the scientific coordinator of ILPnet2, The Network of Excellence in Inductive Logic Programming. He was program co-chair of the 7th International Workshop on Inductive Logic Programming ILP’97 and will be program co-chair of the 16th International Conference on Machine Learning ICML’99. Masayuki Numao, Ph.D.: He is an associate professor at the Department of Computer Science, Tokyo Institute of Technology. He received a bachelor of engineering in electrical and electronics engineering in 1982 and his Ph.D. in computer science in 1987 from Tokyo Institute of Technology. He was a visiting scholar at CSLI, Stanford University from 1989 to 1990. His research interests include Artificial Intelligence, Global Intelligence and Machine Learning. Numao is a member of Information Processing Society of Japan, Japanese Society for Artificial Intelligence, Japanese Cognitive Science Society, Japan Society for Software Science and Technology and AAAI.     

Applying inductive learning to enhance knowledge-based expert systems

This paper describes the use of inductive learning in MARBLE, a knowledge-based expert system I have developed for assisting business loan evaluation. Inductive learning is the process of inferring classification concepts from raw data; I use this technique to generate loan-granting decision rules based on historical and proforma financial information. A learning method is presented in this paper that can induce decision rules from training examples.     

基于粗糙集的归纳推理检索方法

归纳推理检索是基于事例推理（CBR）中常用的检索方法之一，是基于ID3算法的检索方法。文章在基干事例推理方法的背景下论证了事例的属性的重要性和冗余之间的关系，并在此基础上从属性相对于其属性的重要性角度来构造启发函数。和ID3算法相比较，该算法不但降低了计算复杂性，而且在一定程度上可以消除样本中的噪声，使归纳推理检索方法的检索效率有所提高。     

基于ZigBee的无线MI磁强计

在现有医疗磁导航系统和地磁数据的信息检测中,针对磁场测量中遇到的有线传输布线困难及操作不便等情况,利用ZigBee无线收发芯片(CC2430)组成的信息无线传输模块,给出了一种磁场检测无线传输解决方案;在此基础上,通过实验测量,验证ZigBee无线模块对磁感(MI)传感器输出产生影响;确认实际干扰的存在,并将其值量化,提出一套消除无线传输对MI传感器影响的解决方案。     

Inductive logic programming

A new research area, Inductive Logic Programming, is presently emerging. While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of the limitations of its forebears. The background to present developments within this area is discussed and various goals and aspirations for the increasing body of researchers are identified. Inductive Logic Programming needs to be based on sound principles from both Logic and Statistics. On the side of statistical justification of hypotheses we discuss the possible relationship between Algorithmic Complexity theory and Probably-Approximately-Correct (PAC) Learning. In terms of logic we provide a unifying framework for Muggleton and Buntine’s Inverse Resolution (IR) and Plotkin’s Relative Least General Generalisation (RLGG) by rederiving RLGG in terms of IR. This leads to a discussion of the feasibility of extending the RLGG framework to allow for the invention of new predicates, previously discussed only within the context of IR.     

Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL

This paper presents a case study of a machine-aided knowledge discovery process within the general area of drug design. Within drug design, the particular problem of pharmacophore discovery is isolated, and the Inductive Logic Programming (ILP) system progol is applied to the problem of identifying potential pharmacophores for ACE inhibition. The case study reported in this paper supports four general lessons for machine learning and knowledge discovery, as well as more specific lessons for pharmacophore discovery, for Inductive Logic Programming, and for ACE inhibition. The general lessons for machine learning and knowledge discovery are as follows.1. An initial rediscovery step is a useful tool when approaching a new application domain.2. General machine learning heuristics may fail to match the details of an application domain, but it may be possible to successfully apply a heuristic-based algorithm in spite of the mismatch.3. A complete search for all plausible hypotheses can provide useful information to a user, although experimentation may be required to choose between competing hypotheses.4. A declarative knowledge representation facilitates the development and debugging of background knowledge in collaboration with a domain expert, as well as the communication of final results.     

Conformal prediction of biological activity of chemical compounds

The paper presents an application of Conformal Predictors to a chemoinformatics problem of predicting the biological activities of chemical compounds. The paper addresses some specific challenges in this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Results are presented for several non-conformity measures extracted from underlying algorithms and different kernels. A number of performance measures are used in order to demonstrate the flexibility of Inductive Mondrian Conformal Predictors in dealing with such a complex set of data. This approach allowed us to identify the most likely active compounds for a given biological target and present them in a ranking order.     

Learning and plan refinement in a knowledge-based system for automatic speech recognition

This paper shows how a semiautomatic design of a speech recognition system can be done as a planning activity. Recognition performances are used for deciding plan refinement. Inductive learning is performed for setting action preconditions. Experimental results in the recognition of connected letters spoken by 100 speakers are presented.