Showing papers on "Expert system published in 2000"
TL;DR: A review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI) covers the application of expert systems, artificial neural networks (ANNs), and fuzzy logic systems that can be integrated into each other and also with more traditional techniques.
Abstract: This paper presents a review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI). It covers the application of expert systems, artificial neural networks (ANNs), and fuzzy logic systems that can be integrated into each other and also with more traditional techniques. The application of genetic algorithms is considered as well. In general, a diagnostic procedure starts from a fault tree developed on the basis of the physical behavior of the electrical system under consideration. In this phase, the knowledge of well-tested models able to simulate the electrical machine in different fault conditions is fundamental to obtain the patterns characterizing the faults. The fault tree navigation performed by an expert system inference engine leads to the choice of suitable diagnostic indexes, referred to a particular fault, and relevant to build an input data set for specific AI (NNs, fuzzy logic, or neuro-fuzzy) systems. The discussed methodologies, that play a general role in the diagnostic field, are applied to an induction machine, utilizing as input signals the instantaneous voltages and currents. In addition, the supply converter is also considered to incorporate in the diagnostic procedure the most typical failures of power electronic components. A brief description of the various AI techniques is also given; this highlights the advantages and the limitations of using AI techniques. Some applications examples are also discussed and areas for future research are also indicated.
525 citations
TL;DR: The expert system, ISFER, which performs recognition and emotional classification of human facial expression from a still full-face image, consists of two major parts, its inference engine called HERCULES, which converts low level face geometry into high level facial actions, and then this into highest level weighted emotion labels.
477 citations
TL;DR: An approach for detecting vehicles in urban traffic scenes by means of rule-based reasoning on visual data and the synergy between the artificial intelligence techniques of the high-level and the low-level image analysis techniques provides the system with flexibility and robustness.
Abstract: The paper presents an approach for detecting vehicles in urban traffic scenes by means of rule-based reasoning on visual data. The strength of the approach is its formal separation between the low-level image processing modules and the high-level module, which provides a general-purpose knowledge-based framework for tracking vehicles in the scene. The image-processing modules extract visual data from the scene by spatio-temporal analysis during daytime, and by morphological analysis of headlights at night. The high-level module is designed as a forward chaining production rule system, working on symbolic data, i.e., vehicles and their attributes (area, pattern, direction, and others) and exploiting a set of heuristic rules tuned to urban traffic conditions. The synergy between the artificial intelligence techniques of the high-level and the low-level image analysis techniques provides the system with flexibility and robustness.
460 citations
TL;DR: The False Alarm Reduction system is proposed, an alternative real-time infrared-visual system that overcomes the main issue for forest-fire detection systems, and provides the human operator with new software tools to verify alarms.
Abstract: Forest fires cause many environmental disasters, creating economical and ecological damage as well as endangering people's lives. Heightened interest in automatic surveillance and early forest-fire detection has taken precedence over traditional human surveillance because the latter's subjectivity affects detection reliability, which is the main issue for forest-fire detection systems. In current systems, the process is tedious, and human operators must manually validate many false alarms. Our approach, the False Alarm Reduction system, proposes an alternative real-time infrared-visual system that overcomes this problem. The FAR system consists of applying new infrared-image processing techniques and artificial neural networks (ANNs), using additional information from meteorological sensors and from a geographical information database, taking advantage of the information redundancy from visual and infrared cameras through a matching process, and designing a fuzzy expert rule base to develop a decision function. Furthermore, the system provides the human operator with new software tools to verify alarms.
227 citations
TL;DR: An integrated approach for the automatic design of FMS is reported, which uses simulation and multi-criteria decision-making techniques.
164 citations
TL;DR: In this article, a practical expert system for the diagnosis of various faults which may occur in distribution substations is presented, where the backward inexact reasoning process is applied for the fault section estimation using the knowledge of topology, the operation rules of protective devices, heuristic knowledge of well-trained operators, and instantaneous alarms.
Abstract: This paper presents a practical expert system for the diagnosis of various faults which may occur in distribution substations. The backward inexact reasoning process is applied for the fault section estimation using the knowledge of topology, the operation rules of protective devices, heuristic knowledge of well-trained operators, and instantaneous alarms. In this paper, the overall structure, detailed knowledge-base and the efficiency of general methodology based on topology are discussed. The proposed system has been tested in a local control center in Korea as a part of an intelligent guidance system for the SCADA operators.
154 citations
1 Jan 2000
TL;DR: The empirical evidence indicates that the hybrid intelligent system developed to provide a logical process for strategic analysis and help the coupling of strategic analysis with managerial intuition and judgement is helpful and useful in supporting the development of marketing strategy.
Abstract: In this paper, the development of a hybrid intelligent system for developing marketing strategy is described. The hybrid system has been developed to: provide a logical process for strategic analysis; support group assessment of strategic marketing factors; help the coupling of strategic analysis with managerial intuition and judgement; help managers deal with uncertainty and fuzziness; and produce intelligent advice on setting marketing strategy. In this system, the strengths of expert systems, fuzzy logic and artificial neural networks (ANNs) are combined to support the process of marketing strategy development. Moreover, the advantages of Porter's five forces model and the directional policy matrices (DPM) are also integrated to assist strategic analysis. In the paper, the software architecture of the hybrid system is discussed in details. Particularly, the group assessment support module, the fuzzification of strategic factors, and the fuzzy reasoning for setting marketing strategy are addressed. In addition, the empirical field work on evaluating the hybrid system is also summarised. The empirical evidence indicates that the hybrid intelligent system is helpful and useful in supporting the development of marketing strategy.
148 citations
1 Jan 2000
TL;DR: An expert system is developed which can be utilized as an on-line aid to system operators in a distribution SCADA environment and is implemented in Prolog.
Abstract: This paper presents a new application of expert system techniques to the restoration of distribution systems. Primary feeders are typically radial in structure. To increase system reliability, neighboring feeders are connected through a normally open tie switch. When load zones on a feeder are interrupted due to a fault, system operators need to identify neighboring feeders and try to restore customers through the tie switches. To restore maximal number of zones, several steps are followed: group restoration, zone restoration and, if necessary, load transfer. Based on the methodology, an expert system is developed which can be utilized as an on-line aid to system operators in a distribution SCADA environment. The proposed expert system is implemented in Prolog. Implementation issues on knowledge representation, portability of the system, and computational efficiency dre discussed. Several examples are used to illustrate capabilities of the proposed system.
131 citations
TL;DR: A rule induction system based on rough sets and attribute-oriented generalization is introduced and was applied to a database of congenital malformation to extract diagnostic rules and an expert system which makes a differential diagnosis on congenital disorders is developed.
127 citations
TL;DR: A Fuzzy Petri Net notion that combines the graphical power of Petri Nets and the capabilities of FuzzY Sets to model rule-based expert knowledge in a decision support system for railway operation control systems is described.
TL;DR: An integrated fuzzy expert system is presented to diagnose various faults that may occur in a regional transmission network and substations to improve efficiency, generality, and reliability of the solution.
Abstract: An integrated fuzzy expert system is presented to diagnose various faults that may occur in a regional transmission network and substations. Fuzzy reasoning method is applied, and it is discussed in detail. Discrimination of false operations or nonoperations of protective devices as well as the fault identification scheme are also analyzed, together with the fuzzy inference process. The proposed system is designed to improve efficiency, generality, and reliability of the solution. The system will replace a fault diagnosis system that had been tested as a part of an intelligent support system on a local control center in Korea.
TL;DR: A back-up protection scheme for a transmission network that uses an action factor-based expert decision system to provide optimal fault clearance for faults located anywhere on the protected network is described in the paper.
Abstract: A back-up protection scheme for a transmission network is described in the paper. The back-up protection uses an action factor-based expert decision system (referred to as the BPES) to provide optimal fault clearance for faults located anywhere on the protected network. To achieve an optimal response, the BPES needs to know the topology of the network and the operating response of existing protection relays. Based on this information, the expert decision system will try to identify the feeder that contains the fault and which circuit breakers need to be tripped to clear the fault.
TL;DR: A more generalized fuzzy Petri net model for expert systems is proposed, called AFPN (Adaptive Fuzzy Petri Nets), which has both the features of a fuzzyPetri net and the learning ability of a neural network.
Abstract: Knowledge in some fields like Medicine, Science and Engineering is very dynamic because of the continuous contributions of research and development. Therefore, it would be very useful to design knowledge-based systems capable to be adjusted like human cognition and thinking, according to knowledge dynamics. Aiming at this objective, a more generalized fuzzy Petri net model for expert systems is proposed, which is called AFPN (Adaptive Fuzzy Petri Nets). This model has both the features of a fuzzy Petri net and the learning ability of a neural network. Being trained, an AFPN model can be used for dynamic knowledge representation and inference. After the introduction of the AFPN model, the reasoning algorithm and the weight learning algorithm are developed. An example is included as an illustration.
TL;DR: The benefits of a fuzzy object data model for geographical information systems are examined, an overview of the model is presented, and the current prototype implementations are described.
TL;DR: A production rescheduling expert simulation system integrates many techniques and methods, including simulation technique, artificial neural network, expert knowledge and dispatching rules, to solve this ill-structured production problem.
1 Jan 2000
TL;DR: A new approach to applying adaptive neural networks to intrusion detection that is capable of autonomously learning new attacks rapidly through the use of a modified reinforcement learning method that uses feedback from the protected system is presented.
Abstract: The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. Existing intrusion detection approaches require either manual coding of new attacks in expert systems or the complete retraining of a neural network to improve analysis or learn new attacks. This paper presents a new approach to applying adaptive neural networks to intrusion detection that is capable of autonomously learning new attacks rapidly through the use of a modified reinforcement learning method that uses feedback from the protected system. The approach has been demonstrated to be extremely effective in learning new attacks, detecting previously learned attacks in a network data stream, and in autonomously improving its analysis over time using feedback from the protected system.
TL;DR: A lowcost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data to infer facies of the rocks is presented.
Patent•
4 Jan 2000
TL;DR: In this paper, a method and system for creating and managing Virtual Population mutual relationships is described, which uses a Rich Semantic Model component, expert system components, and various interface components and other components to dynamically alter the visitation experience as received by the Visitor at a computer.
Abstract: A method and system for creating and managing Virtual Population mutual relationships is disclosed. The method uses a Rich Semantic Model component, expert system components, and various interface components and other components to dynamically alter the visitation experience as received by the Visitor at a computer and to allow the Visitor control over their Virtual Representative that controls this personal experience.
TL;DR: This review explores the technologies and implementation of the technologies necessary for the development of computer intelligent management systems for enhanced commercial aquaculture production.
TL;DR: A review of several Internet‐based expert systems with a representative sample of publicly available applications, and a discussion of typical tools for developing Internet‐ based expert systems are presented.
Abstract: The Internet offers a large potential for delivery of various information-based services, including the services of intelligent applications. As the availability of the Internet has grown, its value as a medium for the delivery of expert systems in particular has increased. There are now a large number of expert systems available on the Internet, including applications in industry, medicine, science and government. Though the Internet provides several advantages for expert system development, it also presents some special problems. These advantages and disadvantages are explored in more detail in this paper. The paper also presents a review of several Internet-based expert systems with a representative sample of publicly available applications, and a discussion of typical tools for developing Internet-based expert systems. A case study of an Internet-based expert system is presented as well.
TL;DR: The system, IAI-CAPP, integrates fuzzy logic and artificial neural networks to perform the dynamic recognition and adaptive-learning tasks of the workpieces and process plans and adopts the idea of important (critical) feature concept for evaluating the suitability of existing process plans for incoming product designs.
Abstract: This paper presents an integrated artificial intelligent (IAI) system for dynamic computer-aided process planning (CAPP). The system, IAI-CAPP, integrates fuzzy logic (FL) and artificial neural networks (ANN) to perform the dynamic recognition and adaptive-learning tasks of the workpieces and process plans. Also, it adopts the idea of important (critical) feature concept for evaluating the suitability of existing process plans for incoming product designs. In addition, the technique of expert system (ES) is utilized. The system combines variant and generative CAPP and is capable of generating plans that are suitable for workpieces that either are similar to existing workpieces or new. The proposed system described has been realized on a computer prototype program. An illustrative example is also provided.
29 May 2000
TL;DR: Test results show that the fuzzy expert system can forecast future loads with an accuracy comparable to that of neural networks, while it can also incorporate linguistic IF-THEN rules and expert's opinion.
Abstract: This paper presents the development of a fuzzy expert system used to forecast the daily load curve's two minima and two maxima, for each season of the year. The inference operations of fuzzy rules are performed following the Larsen max-product implication method and the product degree of fulfillment method, while the defuzzification procedure is based on the center of area method. The proposed fuzzy expert system for peak load forecasting is tested using historical load and temperature data of the Greek interconnected power system. Test results show that the fuzzy expert system can forecast future loads with an accuracy comparable to that of neural networks, while it can also incorporate linguistic IF-THEN rules and expert's opinion.
TL;DR: The design and implementation of a probabilistic model-based fault diagnosis expert system is described and a rule-based approach is proposed to transform the Bayesian belief network into an acyclic network dynamically during the diagnosis phase to allow simple on-line probability calculation in a belief network with causality loops.
TL;DR: The research finds that ESs in a replacement role prove to be effective for operational and tactical decisions, but have limitations at the strategic level.
Abstract: This paper begins by analysing decision making activities and information requirements at three organizational levels and the characteristics of expert systems (ESs) intended for the two different roles of supporting and replacing a decision maker. It goes on to review the evidence from many years of commercial use of ESs at different levels and in different roles, and to analyse the evidence obtained from a pilot experiment involving developing ESs to fulfil two different roles in the same domain. The research finds that ESs in a replacement role prove to be effective for operational and tactical decisions, but have limitations at the strategic level. ESs in a support role, as advisory systems, can help to make better decisions, but their effectiveness can only be fulfilled through their users. In the experiments, an expert advisory system did not save a user's time, contrary to the expectations of many of its users, but an ES in a replacement role did improve the efficiency of decision making. In addition, the knowledge bases of the ESs in the different roles need to be different. Finally, the practical implications of the experience gained from developing and testing two types of ESs are discussed.
1 Jan 2000
TL;DR: A knowledge structure is developed that generalizes and subsumes production rules, decision trees, and rules with exceptions and gives rise to a natural complexity measure that allows them to be understood, analyzed and compared on a uniform basis.
Abstract: The problem of transforming the knowledge bases of expert systems using induced rules or decision trees into comprehensible knowledge structures is addressed. A knowledge structure is developed that generalizes and subsumes production rules, decision trees, and rules with exceptions. It gives rise to a natural complexity measure that allows them to be understood, analyzed and compared on a uniform basis. The structure is a directed acyclic graph with the semantics that nodes are premises, some of which have attached conclusions, and the arcs are inheritance links with disjunctive multiple inheritance. A detailed example is given of the generation of a range of such structures of equivalent performance for a simple problem, and the complexity measure of a particular structure is shown to relate to its perceived complexity. The simplest structures are generated by an algorithm that factors common sub-premises from the premises of rules. A more complex example of a chess dataset is used to show the value of this technique in generating comprehensible knowledge structures.
Posted Content•
TL;DR: The method of automatic ARIMA modeling (AAM), with and without intervention analysis, that has been used in the analysis is described and the motivation to take part to the M3-Competition is outlined.
Abstract: This article has three objectives: (a) to describe the method of automatic ARIMA modeling (AAM), with and without intervention analysis, that has been used in the analysis; (b) to comment on the results; and (c) to comment on the M3 Competition in general. Starting with a computer program for fitting an ARIMA model and a methodology for building univariate ARIMA models, an expert system has been built, while trying to avoid the pitfalls of most existing software packages. A software package called Time Series Expert TSE-AX is used to build a univariate ARIMA model with or without an intervention analysis. The characteristics of TSE-AX are summarized and, more especially, its automatic ARIMA modeling method. The motivation to take part in the M3-Competition is also outlined. The methodology is described mainly in three technical appendices: ( Appendix A ) choice of differences and of a transformation, use of intervention analysis; ( Appendix B ) available specification procedures; ( Appendix C ) adequacy, model checking and new specification. The problems raised by outliers are discussed, in particular how close they are from the forecast origin. Several series that are difficult to deal with from that point of view are mentioned and one of them is shown. In the last section, we comment on contextual information, the idea of an e−M Competition, prediction intervals and the possible use of other forecasting methods within Time Series Expert.
Patent•
10 Jul 2000TL;DR: In this article, a technique for qualification of loops for new digital subscriber line services (DSL) involves use of an expert system, such as a neural network, to predict performance for future loops.
Abstract: A technique for qualification of loops for new digital subscriber line services (DSL) involves use of an expert system, such as a neural network. A database of loop characteristic information and performance data enables the expert system to train or learn how to predict performance for future loops. In response to data characterizing a new loop to be qualified, the trained expert system predicts digital subscriber line performance for the new loop. Typically, the prediction enables classification of service capacity for the new loop into one of several classes corresponding to levels of DSL service offered through the network. The database for use by the expert system is updated as each newly qualified loop is brought into service and actual performance for that loop is known.
28 Aug 2000
TL;DR: New cooperative components integrated in the ACE framework that support cooperative knowledge/data collection, expert finding and learner group organization are introduced.
Abstract: The Adaptive Courseware Environment (ACE) [1] is a WWW-based tutoring framework which combines methods of knowledge representation, instructional planning, and adaptive media generation to deliver individualized courseware over the WWW. In this paper we like to introduce new cooperative components integrated in the ACE framework that support cooperative knowledge/data collection, expert finding and learner group organization. All these new cooperative components are realized with modified components of the BSCW environment [2]. A pedagogical agent in ACE is able to make recommendations for switching between individual and cooperative learning activities and find advanced or expert students in relation to one's own profile.
Patent•
31 Aug 2000TL;DR: In this paper, an expert system and a method of providing automated advice are described, where the system is regularly updated by advice (or diagnosis, recommendation etc.) given by practitioners in the relevant field.
Abstract: An expert system and a method of providing automated advice are described. The system is regularly updated by advice (or diagnosis, recommendation etc.) given by practitioners in the relevant field. The combination of the underlying facts and the consequent (human) advice is used to update a ruleset that is then used to provide automated advice. An example of financial advice is given. A database contains the details for the customers of a financial institution together with advice and recommendations given previously by the institution's human advisors. This database is used to derive a ruleset which is then applied to a subsequent customer's details in an automated manner, possibly at the user's own PC via the World Wide Web. Frequent updating using data from the human advisors' work means that the database (and hence the ruleset) are kept up to date. Consistent advice can thus be provided with minimum human interaction.