Showing papers on "Expert system published in 2003"
TL;DR: Tetracorder as discussed by the authors is a decision tree-based approach for spectral identification and mapping of materials based on a set of expert system rules that describe which diagnostic spectral features are used in the decision-making process.
Abstract: [1] Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision-making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.
884 citations
TL;DR: How AI techniques might play an important role in modeling and prediction of the performance and control of combustion process is illustrated to testify to the potential of AI as a design tool in many areas of combustion engineering.
635 citations
TL;DR: Describing of how three kinds of cognitive tools—semantic networks, expert systems, and systems modeling tools—can be used to externalize learner’s internal representations are provided are provided.
Abstract: The premise of this paper is that the key to problem solving is adequately representing the problem to be solved. Most research has focused on how problems are (re)presented to learners. The assumption that those external representations naturally map onto learners’ internal representations of problems has not been confirmed. New research has examined the role of tools for externalizing learners’ internal representations. Descriptions of how three kinds of cognitive tools—semantic networks, expert systems, and systems modeling tools—can be used to externalize learner’s internal representations are provided. Research is needed to study the efficacy of these tools for supporting problem solving.
338 citations
TL;DR: The most important steps of this process of application of pattern recognition techniques, expert systems, artificial neural networks, fuzzy systems and nowadays hybrid artificial intelligence techniques in manufacturing are outlined and some new results are introduced with special emphasis on hybrid AI and multistrategy machine learning approaches.
216 citations
TL;DR: The fuzzy model successfully demonstrated the potentials of exploring “embedded information” by combining data mining techniques with heuristic knowledge to predict algal biomass concentration in the eutrophic Taihu Lake, China.
163 citations
Book•
1 Jan 2003TL;DR: This treatise aims to provide a logical basis for praxeic reasoning for game theory basics and probability theory basics as well as other topics.
Abstract: In our day-to-day lives we constantly make decisions which are simply 'good enough' rather than optimal Most computer-based decision-making algorithms, on the other hand, doggedly seek only the optimal solution based on rigid criteria and reject any others In this book, Professor Stirling outlines an alternative approach, using novel algorithms and techniques which can be used to find satisficing solutions Building on traditional decision and game theory, these techniques allow decision-making systems to cope with more subtle situations where self and group interests conflict, perfect solutions can't be found and human issues need to be taken into account - in short, more closely modelling the way humans make decisions The book will therefore be of great interest to engineers, computer scientists and mathematicians working on artificial intelligence and expert systems
133 citations
7 Oct 2003
TL;DR: A review of developments in applications of artificial intelligence techniques for induction machine stator fault diagnostics, including fault diagnosis of electric motor drive systems using AI techniques has been considered.
Abstract: The on-line fault diagnostics technology for induction machines is fast emerging for the detection of incipient faults as to avoid the unexpected failure. Approximately 30-40 % faults of induction machines are stator faults. This paper presents a review of developments in applications of artificial intelligence techniques for induction machine stator fault diagnostics. Now a days artificial intelligence (AI) techniques are being preferred over traditional protective relays for fault diagnostics of induction machines. The application of expert system, fuzzy logic system, artificial neural networks, genetic algorithm have been considered for fault diagnostics. These systems and techniques can be integrated into each other with more traditional techniques. A brief description of various AI techniques highlighting the merits and demerits of each other have been discussed. Fault diagnosis of electric motor drive systems using AI techniques has been considered. The futuristic trends are also indicated.
110 citations
TL;DR: The viability of HAS as an effective procedure for hotel selection has been ascertained by the positive feedback obtained from the survey questionnaires and support the contention that HAS performs its functions as expected.
Abstract: In this paper, we describe the research and development of a fuzzy expert system for hotel selection. A prototype system, called hotel advisory system (HAS), has been designed and developed to assist tourists in conducting hotel selection using fuzzy logic. HAS is implemented on personal computers under a Microsoft WindowsTM environment. To evaluate the performance of HAS, selected practitioners in the Hong Kong hotel industry and potential users from twelve nations were invited to participate in testing the system. The potential users and hotel experts rated highly on the effectiveness and the usability of the system. The results of the prototype evaluation were satisfactory and support the contention that HAS performs its functions as expected. The viability of HAS as an effective procedure for hotel selection has been ascertained by the positive feedback obtained from the survey questionnaires. Using HAS makes hotel selection simple because it can incorporate the linguistic terms which are normally produced by tourists.
103 citations
1 Jul 2003
TL;DR: The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells.
Abstract: Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).
99 citations
TL;DR: DIARES-IPM is an operational automatic identification tool that helps non-experts to identify pests and suggest the appropriate treatments and it includes the most economically important diseases, insects and nutritional deficiencies that affect these crops.
96 citations
TL;DR: In this paper, a preterm risk prediction case study illustrates the opportunities and describes typical data mining issues in the nontrivial task of building knowledge in nursing, using data mining or any other method, will make progress only if important data that capture expert nurses' contributions are available in clinical information systems configurations.
TL;DR: It is concluded that the procedure outlined in this paper suitably deals with criticism regarding MFs and, therefore, enables a practical implementation of fuzzy evaluation of agricultural production systems.
23 Jun 2003
TL;DR: A Maximum Likelihood Hebbian Learning-based method that automates the organisation of cases and the retrieval stage of case-based reasoning systems and has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.
Abstract: CBR systems are normally used to assist experts in the resolution of problems. During the last few years researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a Maximum Likelihood Hebbian Learning-based method that automates the organisation of cases and the retrieval stage of case-based reasoning systems. The proposed methodology has been derived as an extension of the Principal Component Analysis, and groups similar cases, identifying clusters automatically in a data set in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.
TL;DR: A new approach for developing a concrete bridge rating expert system for deteriorated concrete bridges, constructed from multi-layer neural networks, that performs fuzzy inference in the network, facilitates refinement of the knowledge base by use of the back-propagation method, and prevents the inference mechanism of the expert system from becoming a black box.
TL;DR: Diagnostic technology for photovoltaic (PV) systems was developed, using the learning method to take each site’s conditions into account, and greatly simplifies the servicing and maintenance of PV systems.
TL;DR: The pertinent literature shows that nearly all the researches in this field have only focussed on the automation of monitoring the process, and the remaining two tasks still need to be carried out by quality practitioners.
Abstract: Statistical process control (SPC) is one of the most effective tools of total quality management, the main function of which is to monitor and minimize process variations. Typically, SPC applications involve three major tasks in sequence: (1) monitoring the process, (2) diagnosing the deviated process and (3) taking corrective action. With the movement towards a computer integrated manufacturing environment, computer based applications need to be developed to implement the various SPC tasks automatically. However, the pertinent literature shows that nearly all the researches in this field have only focussed on the automation of monitoring the process. The remaining two tasks still need to be carried out by quality practitioners.
This project aims to apply a hybrid artificial intelligence technique in building a real time SPC system, in which an artificial neural network based control chart monitoring sub-system and an expert system based control chart alarm interpretation sub-system are integrated for automatically implementing the SPC tasks comprehensively. This system was designed to provide the quality practitioner with three kinds of information related to the current status of the process: (1) status of the process (in-control or out-of-control). If out-of-control, an alarm will be signaled, (2) plausible causes for the out-of-control situation and (3) effective actions against the out-of-control situation. An example is provided to demonstrate that hybrid intelligence can be usefully applied for solving the problems in a real time SPC system. Copyright © 2003 John Wiley & Sons, Ltd.
Patent•
28 Oct 2003
TL;DR: In this article, a user can specify states of influence variables with information from an expert system to perform assessment regarding probable damages caused by a terrorist attack to a property to which insurance premiums are to be established.
Abstract: Systems and methods allow a user to specify states of influence variables with information from an expert system to perform assessment regarding probable damages caused by a terrorist attack to a property to which insurance premiums are to be established. The expert system provides information based on knowledge of terrorists, including their goals, methods, organization and financial structure. The systems and methods use quality information to establish a relevant set of variables and to subjectively define the probabilistic influences of the defined variables on the likelihood of attack and levels of damage.
Patent•
21 Aug 2003
TL;DR: In this article, the authors present a system and method for valuation of complex systems, which includes a data management system that collects and stores data and results; an expert system that analyzes the data; and an integration system that aggregates all appearing quantities including their uncertainties.
Abstract: A system and method are for valuation of complex systems. As a result, a detailed and complete assessment of the current and future state of a complex system can take place. The system and method provide a fully objective, transparent, and accurate way for valuing a complex system because the valuation result is calculated as the integration of detailed valuations of the complex system's constituents. The system and method further provide a complete and consistent treatment of the uncertainties associated with future expectations. The system and method include a structuring method that divides the complex system into representative constituents; a data management system that can collect and store data and results; an expert system that can analyze the data, and; an integration system that can aggregate all appearing quantities including their uncertainties. As optional part it also includes an optimization system and method.
1 Jan 2003
TL;DR: The development of a type-2 FLS for UAB assessment will provide the capability to handle linguistic uncertainties better and will lead to the creation of mechanisms to allow the system to adapt to individual experts decision-making.
Abstract: In this paper, we describe the development of a type-2 Fuzzy Logic System (FLS) based expert system for Umbilical Acid-Base (UAB) assessment. The aim of this work is to develop an expert system which can adapt to an individual experts decision making mechanism by determining the parameters that define the uncertainties of the terms used. Umbilical acid-base assessment of a newborn infant can provide vital information on the infants health and guide requirements for neonatal care. However, there are problems with the technique. Blood samples used for UAB assessment frequently contain errors in one or more of the important parameters, preventing accurate interpretation and many clinical staff lack the expert knowledge required to interpret error-free results. A type-1 FLS-based expert system was previously developed and implemented to overcome these difficulties [1]. However, it was observed that the type-1 fuzzy expert system was not capable of fully capturing the linguistic uncertainties in the terms used and the inconsistency of the experts decision making. Type-2 FLSs offer better capabilities to handle linguistic uncertainties by modelling the uncertainties using type-2 membership functions and provide diagnosticians with decision-making flexibilities. The development of a type-2 FLS for UAB assessment will provide the capability to handle linguistic uncertainties better and will lead to the creation of mechanisms to allow the system to adapt to individual experts decision-making. Such a system would truely be a smart adaptive fuzzy expert system.
TL;DR: This research proposes a CBR approach that employs the k-nearest neighbors concept with dynamic feature weights and non-linear similarity functions that can more accurately predict order due dates than other approaches.
Abstract: Owing to the complexity of wafer fabrication, the traditional human approach to assigning due-date is imprecise and very prone to failure, especially when the shop status is dynamically changing. Therefore, assigning a due date to each order becomes a challenge to the production planning and scheduling staff. Since most production orders are similar to those previously manufactured, the case based reasoning (CBR) approach provides a suitable means for solving the due-date assignment problem. This research proposes a CBR approach that employs the k-nearest neighbors concept with dynamic feature weights and non-linear similarity functions. The test results show that the proposed approach can more accurately predict order due dates than other approaches.
TL;DR: This work uses a vision system, robot arm and mechanical hand to locate and manipulate unmodeled, randomly placed objects of various sizes and shapes and uses computational geometry to gauge the quality of the grasp and to quantify and validate the choice of hand configurations generated by the fuzzy logic expert system.
Abstract: We investigate "intelligent" grasping schemes using a fuzzy logic rule base expert system. We use a vision system, robot arm and mechanical hand to locate and manipulate unmodeled, randomly placed objects of various sizes and shapes. In the pregrasp stage, we use vision data to provide a nonlinear mapping from object characteristics to hand configuration. In the postgrasp stage, we use hand data to ascertain the security of the grasp. Computational geometry is used to gauge the quality of the grasp and to quantify and validate the choice of hand configurations generated by the fuzzy logic expert system. The system is implemented within a low-cost virtual collaborative environment.
IBM1
TL;DR: The idea of modal keywords, which are keywords that represent perceptual concepts in the following categories: visual, auditory, aural, tactile, and taste, are introduced.
Abstract: We proposed a novel framework for video content understanding that uses rules constructed from knowledge bases and multimedia ontologies. Our framework consists of an expert system that uses a rule-based engine, domain knowledge, visual detectors (for objects and scenes), and metadata (text from automatic speech recognition, related text, etc.). We introduce the idea of modal keywords, which are keywords that represent perceptual concepts in the following categories: visual (e.g., sky), aural (e.g., scream), olfactory (e.g., vanilla), tactile (e.g., feather), and taste (e.g., candy). A method is presented to automatically classify keywords from speech recognition, queries, or related text into these categories using WordNet and TGM I. For video understanding, the following operations are performed automatically: scene cut detection, automatic speech recognition, feature extraction, and visual detection (e.g., sky, face, indoor). These operation results are used in our system by a rule-based engine that uses context information (e.g., text from speech) to enhance visual detection results. We discuss semi-automatic construction of multimedia ontologies and present experiments in which visual detector outputs are modified by simple rules that use context information available with the video.
TL;DR: A computer system solution for integration of a distributed bioreactor monitoring and control instrumentation on the laboratory scale is described and demonstrated on different cultivations/fermentations, showing high operational stability and reliable function and meet typical requirements for production safety and quality.
TL;DR: An efficient expert system for machine fault diagnosis is developed and a new search method is proposed in this system to improve the efficiency of the diagnostic process.
Abstract: An efficient expert system for machine fault diagnosis is developed. A new search method is proposed in this system to improve the efficiency of the diagnostic process. First of all, a diagnostic tree (a decision tree) is built by domain experts according to the functions of the devices in the machine. Then, the diagnostic priorities of nodes (devices) in the tree are determined based on a fuzzy group multiple attribute decision making method. A meta knowledge base for fault diagnosis is generated automatically based on the determined priorities to guide the diagnostic process. After that, a domain knowledge base that hypothesises possible faults for each device in the tree is generated by domain experts and/or manuals. At last, the inference process starts based on the meta knowledge base and hypothesises which device is the possible cause of failure. To validate the system performance, an illustrative example (VCR troubleshooting) is presented for demonstration purposes.
Journal Article•
TL;DR: In this article, the main aim of reactive power control is to provide appropriate placement of compensation devices to ensure a satisfactory voltage profile while minimizing the cost of compensation, and the focus has shifted away from the methodology of formal mathematical modelling, which was suited to the fields of operation research, control theory, and numerical analysis, to the less rigorous techniques of artificial intelligence (AI).
Abstract: The main aim of reactive power control is to provide appropriate placement of compensation devices to ensure a satisfactory voltage profile while minimizing the cost of compensation. Since the early to middle 1980s, the focus has shifted away from the methodology of formal mathematical modelling, which was suited to the fields of operation research, control theory, and numerical analysis, to the less rigorous techniques of artificial intelligence (AI). This article reviews AI methods: expert systems, fuzzy systems, artificial neural networks, evolutionary computing, and tabu search for reactive power/voltage control in power systems. Hybrid systems for reactive power control are also critically reviewed.
TL;DR: A novel approach for developing a performance evaluation system for concrete slabs of existing bridges that performs inference in the network, facilitates refinement of the knowledge base embedded in the system by the back propagation method, and prevents not only the inference mechanism of the system but also knowledge base after machine learning from becoming a black box.
TL;DR: Reveals that, although still regarded as a novel methodology, ES are shown to have matured to the point of offering real practical benefits in many of their applications.
Abstract: Intelligent solutions, based on expert systems (ES), to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. Expert systems are being developed and deployed worldwide in myriad applications, mainly because of their symbolic reasoning and its explanation capabilities. Provides an overview for the operations researcher of the expert systems methodology, as well as their historical and current use in business. Aims to present and focus on the wide range of business areas of ES applications, avoiding an in‐depth analysis of all the applications ‐‐ with varying success ‐‐ recorded in the literature. Reveals that, although still regarded as a novel methodology, ES are shown to have matured to the point of offering real practical benefits in many of their applications.
Book•
1 Jan 2003
TL;DR: The focus of intelligent condition monitoring and diagnosis system is on practical applications of intelligent techniques and the text provides practicing engineers and scientists with the information they need to solve the problems in both industry and academia.
Abstract: This work covers intelligent system development. In order to survive in an uncertain environment, it is necessary to bring artificial neural networks, fuzzy logic systems, genetic algorithms and expert systems together to make a condition monitoring and diagnosis system more reliable and cost effective than a traditional one. The focus of intelligent condition monitoring and diagnosis system is on practical applications of intelligent techniques. The text provides practicing engineers and scientists with the information they need to solve the problems in both industry and academia.
Patent•
18 Sep 2003
TL;DR: In this article, an expert system having a data storage device and a processor is described, where the processor evaluates a selected model in accordance with a selected objective and has the variable attribute set in accordance to a selected strategy to determine a characteristic value associated with the selected model and variable attribute setting.
Abstract: An expert system having a data storage device and a processor. The data storage device stores models having attributes, objectives having rules for evaluating the models, and strategies having rules for modifying the attributes. The processor evaluates a selected model in accordance with a selected objective and having the variable attribute set in accordance with a selected strategy to determine a characteristic value associated with the selected model and the variable attribute setting. The processor also stores information associated with improved results in the data storage device.