Intelligent analysis and pattern recognition in cardiotocographic signals using a tightly coupled hybrid system

In obstetrics, cardiotocograph (CTG) and non-stress test readings are indispensable to antenatal monitoring and assessment. Difficulties in the interpretation of CTG records require methods for computer-assisted analysis. This article describes CAFE (Computer Aided Foetal Evaluator), an intelligent tightly coupled hybrid system developed to overcome the difficulties inherent in CTG analysis. It integrates algorithms (implemented via conventional programming techniques) with Artificial Intelligence (AI) paradigms (rule-based systems and artificial neural networks), in order to automate and perform all the phases involved in real time antenatal monitoring, from the analysis and interpretation of CTG signals to diagnosis. Its architecture, components and functional character will be described in detail. The validation of CAFE over 3450 minutes of signal time corresponding to 53 different patients in a real environment is discussed, and its performance with respect to a group of experts is evaluated. Most of the results obtained reflect acceptable levels of performance—equivalent to expert performance—and thus confirm the suitability of AI techniques to applications in this field.     

Symbolical Reasoning about Numerical Data: A Hybrid Approach

By combining methods from artificial intelligence and signal analysis, we have developed a hybrid system for medical diagnosis. The core of the system is a fuzzy expert system with a dual source knowledge base. Two sets of rules are acquired, automatically from given examples and indirectly formulated by the physician. A fuzzy neural network serves to learn from sample data and allows to extract fuzzy rules for the knowledge base. A complex signal transformation preprocesses the digital data a priori to the symbolic representation. Results demonstrate the high accuracy of the system in the field of diagnosing electroencephalograms where it outperforms the visual diagnosis by a human expert for some phenomena.     

Machine analysis of acoustical signals

Research toward machine analysis of acoustical signals is described. The approach is that of the expert system, where knowledge about the physical world that produced the signals is used by the system in the interpretation process. Expert systems have already matched human capabilities in applications such as chemical structure analysis and certain areas of medical diagnosis, suggesting that acoustical signal analysis will meet with similar success. The paper describes aspects of knowledge representation and signal processing methods of an experimental computer program that is being used as a vehicle for development of AI techniques in the processing of acoustic signals.     

Quality control expert systems: a review of pertinent literature

Statistical quality control (SQC) is an effective tool that ensures quality products and services by means of control charts, the essence of SQC, and sampling plans. While the computation of sample statistics and the development of control charts are routine exercises, the interpretation of chart patterns, trends and the associated diagnosis of assignable causes requires expert knowledge. The present trend is to develop a quality control system and apply it throughout the company (company-wide quality control CWQC or total quality control - TQC). This frequently means involvement of non-quality personnel in QC teams. Additionally, many companies are faced with a shortage of experienced quality controllers and individuals who can train and educate others on statistical quality control techniques. Quality control expert systems (QCESs) are considered as one way to alleviate these difficulties. In recent years, quality control expert systems have attracted the attention of both quality researchers and practitioners. This paper reviews existing quality control expert systems and recommends a set of quality engineering techniques that should be used to form a knowledge base, the heart of an expert system.     

Case-based reasoning for interpretation of data from non-destructive testing

Non-destructive testing (NDT) is a name for a range of methods and procedures used to determine fitness of industrial products for further use. The use of NDT testing techniques results in data in the form of signals, images, or sequences of these, which have to be analysed in order to determine if they contain any indications of defects in the inspected objects. This analysis is often quite complex. In the past, systems have been built which used neural networks (and other statistical classifiers) as well as expert systems to interpret NDT data; however, successful uses of these systems in inspection practice are rare. This article presents how the case-based reasoning methodology (where interpretation of new data is based on previous data-interpretation cases) can be used to tackle the problem of NDT data interpretation. The article presents the characteristics of CBR, which make it an interesting alternative to statistical classifiers and to expert systems. Suitability of CBR for NDT data interpretation is illustrated based on examples of two applications: a CBR system for ultrasonic rail inspection and a CBR system for eddy-current inspection of heat exchangers.     

化工过程实时状态监测与模糊诊断系统研究与实现

过程监测与故障诊断是化工过程工程中的一项重要任务，是保证生产过程安全运行、降低废品率的重要环节。本文在分析各种诊断方法的基础上，结合数据分析、专家系统、模糊逻辑以及面向对象分析等技术，提出并开发了一个化工过程实时监测与模糊诊断系统。该系统由数据校正、过程监测和模糊专家系统三部分组成，采用开放式结构，具有良好的易维护性和可扩充性。     

AMPERES: A REAL-TIME FAULT MONITORING AND DIAGNOSIS KNOWLEDGE-BASED SYSTEM FOR SPACE POWER SYSTEMS

A real-time fault monitoring and diagnosis knowledge-based system (KBS) has been developed for space power systems to save costly operational manpower and to achieve reliable space power system operation. The developed KBS, called AMPERES (autonomously managed power-system extendible real-time expert system), was developed using the autonomously managed power system (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC). The approaches taken for AMPERES are generic and should be applicable for other space power systems. A sensor value validation method based on causal relations (CRs) and their interrelations within the sensor redundancy graphs (SRG) has been developed and applied in AMPERES to increase the confidence in the validity of the sensor values and consequently in the results of the fault diagnosis. The validation results also yield essential information for fault diagnosis identifying the existence of a potential fault and the location or the extent of the fault. A disturbance interrelation analysis graph (DIAG) scheme has been developed to characterize the fault diagnosis problems clearly and to perform the fault monitoring and diagnosis tasks effectively by explicitly showing the diagnosis reasoning paths. The DIAG approach also enables the fault diagnosis KBS to describe a fault in several different abstraction levels and in different component granularities.     

基于网络的武器系统远程故障诊断技术研究

对武器装备性能的故障诊断,传统的方法是使用多种检测仪器对武器装备进行性能检查,技术含量低,不能满足现代战争的需要;为了提高部队的作战性能,研制了基于C/S模式的网络化远程测试系统,该系统主要利用基于USB接口和RS232接口的虚拟仪器和基于LAN接口的网络化仪器实现对信号的远程采集、处理和实时显示;同时系统引入了网络监控设备,专家端和现场端方便实时地进行视频和音频信息传递;该装置在实际应用中取得了良好效果,降低了维修保障费用,具有较高的军事及经济意义.     

Patient management in CCUs: Need for an intelligent interpretation of signals

In this work we are going to approach, within the field of intensive surveillance of patients, what has been termed intelligent monitoring, its implications in nonclassical signal processing schemes, the role of the expert systems from this perspective, and the current development of a system for intelligent monitoring (SUTIL) aimed at the environment of coronary care units (CCUs).     

Building a financial diagnosis system based on fuzzy logic production system

The purpose of this study is to build a financial expert system based on fuzzy theory and Fuzzy LOgic Production System (FLOPS), which is an expert tool for processing the ambiguity. The study consists if four parts. For the first part, the basic features of expert systems are presented. For the second part, fizzy concepts and the evaluation of classical expert systems to fuzzy expert systems will be presented. For the third part, the expert system shell (FLOPS) used in this study will be described. For the last part, it will be presented the financial diagnosis system, developed by using the Wall's seven ratios, traditional seven ratios and also 34 ratios selected by a financial expert. After analyzing and investigating these three kinds of methods, financial diagnosis system will be developed as a fuzzy expen system which used a membership function based on averages and standard deviation. At the last step, the new approach will be tried by increasing the fuzzy sets for five membership functions. Some practical examples will be given. Throughout the paper, the way of building a financial diagnosis system based on fuzzy expert system is stressed.     

A Software Tool for Constructing Traditional Chinese Medical Expert Systems

It is an urgent task to hnplemeut a lot of expert systems to capture the valuable expertise ofexperienced doctors of traditional Chinese medicine.In order to meet the needs,a software tool isdeveloped.It features a unified diagnosis model,a specially designed knowledge representationlanguage and an efficient but effective inference engine.To implement an expert system,it isonly necessary to input the expert's knowledge expressed in knowledge representation languagewithout the design of any additional software.The time and effort required for implementing anexpert system are thus greatly saved.The software is very compact and can run onmicrocomputers e.g.IBM-PC/XT.Two traditional Chinese medical expert systems have beensuccessfully implemented with the tool.     

故障诊断专家系统及其发展

文章对主要的故障诊断专家系统进行了系统的归纳和分类,主要关注故障诊断专家系统在军事领域的应用;重点讨论了基于规则的诊断专家系统、基于模型的诊断专家系统、基于人工神经网络的诊断专家系统、基于模糊推理的诊断专家系统和基于事例的诊断专家系统的技术要点、发展现状、优缺点及其在军事方面的应用;最后,对该学科的发展做出了预测,指出基于多种模型结合的诊断专家系统、分布式诊断专家系统、实时诊断专家系统是今后的发展方向。     

A gene mapping expert system

Expert systems are now commonly developed to solve practical problems. Nevertheless, genetics has just begun to benefit from this new technology, since genetic expert systems are extremely rare and often purely experimental. A prototype for risk calculation in pedigrees was developed at the University of Utah, using a commercial frames/rules developmental shell (Intelligence Compiler), which runs on an IBM PC. When small data sets were used, the implementation functioned well, but it could not handle larger data sets. Performance became a major issue, with two possible solutions. The first possibility would have been to port the system to a more powerful machine, and the second would have been to use several different shells or languages, each efficiently representing a specific type of knowledge. Neither of these solutions was applicable in this case. From this experience, we learned that performance, portability, and modifiability were three major requirements for genetic expert systems. To achieve these goals, we implemented the gene mapping expert system GMES: (GMES is unrelated to the gene mapping system, GMS in Lisp combined with a frame/object shell (FROBS). We were able to efficiently represent, control, and optimize a gene mapping experiment, achieving portability by building GMES on top of a C-based version of Common Lisp. Lisp combined with the FROBS expert system shell permitted a declarative representation of each of the components of the experiment, resulting in a transplant specification of the problem within a maintainable system.     

Expert systems for legal decision making

Abstract: AI technology is being used to develop expert systems that solve complex problems in the legal area. Most of these systems employ rules to describe the strategies and procedures used by litigators to analyze legal issues. The tasks performed by these systems include interpreting the law, anticipating the legal consequences of proposed actions, predicting the effects of changes in legislation, as well as analyzing and managing cases. The special characteristics of the legal domain cause certain problems for expert system builders. We discuss some of these problems and describe LDS and SAL, two expert systems we have developed for case evaluation and settlement in the product liability area. SAL (System for Asbestos Litigation) evolved from the earlier and more general expert system, LDS (Legal Decisionmaking System). We also describe XPL, an explanation facility we developed for use by SAL and other expert system applications.     

专家系统在铁路信号设备故障诊断中的应用

铁路信号设备在保证列车运行,提高运输效率,保证铁路运行秩序等方面起到重要作用,同时也成为保障铁路运输安全的关键所在。当前,对铁路信号设备故障的分析处理大多依赖于设备维护人员的经验,由于铁路大发展所造成的专业人员数量相对缺乏,有必要建造一个专家系统,作为铁路信号设备故障诊断的有效辅助工具。通过研究专家系统的一般原理,设计出一种适合铁路信号设备故障诊断的专家系统。通过铁路现场调试表明,该专家系统适用于国内铁路信号系统,对保障铁路运输安全有重大意义。     

冷轧生产线实时故障诊断专家系统的研究与实现

本文介绍了对某钢厂冷轧大型自动化生产线进行状态监测和故障诊断的实时故障诊断专家系统的研究与实现 ,为实时专家系统应用于复杂的动态大系统 ,提供了一个成功的范例。对研究类似的实时故障诊断专家系统将具有重要启发和实际参考     

一种通用型武器系统故障诊断与测试平台的研究

为了解决武器系统故障诊断系统繁多，重复开发和研制的经费投入过多等问题，提出了硬件采用PC104结构的主控计算机加各种型号装备的信号测试接口箱，软件综合采用专家系统、神经网络以及信号处理等故障诊断方法的平台研制方案。抽象了武器系统的系统模型，针对武器系统没有预留诊断接口、不能频繁地插拔连接器等特殊性，总结了尽量在与武器系统不发生物理连接的情况下定位故障以及故障排查的顺序是先电控系统后液压系统等平台建立的原则。该平台在实际装备上试用，取得了良好效果。     

Evaluation of the Use of Induction in the Development of a Medical Expert System

In an earlier study, two medical expert systems for diagnosing thyroid disorders, developed by the application of induction on a sample of previously diagnosed cases and on expert-generated rules, diagnosed a set of test cases better than an expert system developed by the more traditional method of collaboration between a knowledge engineer and an expert. In this paper, an alternative measure of the accuracy of diagnosis of each system is used to evaluate the systems. Diagnoses for every distinct case represented by a combination of indicating factors are compared with diagnoses that the expert made. The induced systems provide diagnoses for many more distinct cases, but a much higher proportion of these diagnoses are incorrect. It is argued that generalizing to unseen cases is an inappropriate use of induction algorithms. The systematic development of a decision table is a more appropriate method for devising a medical expert system.     

Evaluation of expert systems for automatic shape recognition by ultrasound

This paper presents an evaluation of several types of expert systems in automatic object recognition for robotic manipulators, using ultrasound. In fact, rule-based expert systems and probabilistic expert systems have been compared in a calculation of prismatic body orientation and object shape recognition. Furthermore, both types of expert systems were used to distinguish different piece shapes and to detect object position in a real scenario using a robotic manipulator. Information for the automatic recognition system is provided to the expert system by means of the ultrasonic signal coming back from the illuminated object, which is captured by only one receiver placed on the robot grip. Subsequently, in order to reduce the number of parameters to work with, a parametric method for characterisation of this signal is presented. This has been done by calculating several geometric parameters from the signal envelope. Afterwards, a study of the probability distribution function for each parameter provides the necessary information for the expert system to carry out the distinction between the different objects of interest. In this way, it permits the establishment of a comparison among different expert system types for automatic shape recognition using ultrasounds.     

Model-based remotely-sensed imagery interpretation

Abstract

We present a model-based remotely-sensed image interpretation expert system embeded in a knowledge-based geographic information system (K. BIS). The KBIS consists of four sub-systems: a pictorial data base system, an image interpretation expert system, a computer-aided planning system and a computer-aided cartographic system. The image interpretation expert system represents ecological knowledge and other expert knowledge by frames. Its reasoning process consists of a forward reasoning based on the Bayes classification of Landsat imagery, a backward reasoning using frame knowledge and reasoning using a spatial consistency model. A forest inventory study was conducted in Shaxian county, in the southern part of China, using this expert system. The results have shown a significant improvement. Building image interpretation expert systems within knowledge-based pictorial systems is very convenient and efficient because there are well-organized data, knowledge and procedures available.