Diagnosing the vibrating behaviour of the PWR internal structures using expert systems techniques and algorithmic treatment

Expert systems are a well-known and well-received technology. It was thought that the performance of a domain expert could not be duplicated by a machine. Expert systems technologies have shown this to be a false belief, and indeed have demonstrated how experts themselves can come to depend on expert systems. Expert systems enjoy widespread use in industrial domains and further uses are planned. The growth in acceptance has been explosive since about 1986. Continued rampant growth appears to depend on cracking the so-called knowledge acquisition bottleneck.The knowledge acquisition bottleneck limits the scalability of expert systems. While it is relatively straightforward to populate a small-scale knowledge base, it becomes more difficult to maintain consistency and validity as the knowledge base grows. Thus, it is important to automate the knowledge acquisition process. A by-product of this process is that any failure of the expert system will be “soft.”The question is, “What impact can machine learning technologies have on knowledge acquisition in the large?” The true test will be on prospective industrial applications in areas such as biology, education, geology, medicine, and scientific discovery. Machine learning technologies include expert systems, genetic algorithms, neural networks, random seeded crystal learning, or any effective combinations.Relevant subtopics include: Second generation expert systems — progress and prognosisRepertory GridsThe importance of symbolic and qualitative reasoningThe acquisition of fuzzy rulesThe best learning paradigm or combination of paradigmsImpact of machine learning on explanation systemsThe role of toy domains such as chessAutomatic programming revisitedApplications to computer vision, decision support systems, diagnosis, helpdesks, optimization, planning, scheduling, et al.Implementation issues using SIMD and MIMD platformsSources for joint sponsorshipForming industrial partnershipsForming alliances abroad

A framework for the integration of expert systems with multimedia technologies

Estimation of noise power spectral densities from the Monte-Carlo simulated terminal currents in semiconductor devices

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.

This paper presents the way that expert system technology has been used for the development of a consulting tool in the National Technical University of Athens, aiming to support those who want to apply Production Planning and Control (PPC) software in their manufacturing firm. In the knowledge base that has been created, both a production systems taxonomy and a generic software package with specific modules and functions are presented. The inferences are based on rules, that express the need for the existence of certain software, when a set of production systems characteristics is found in the production system that is under consideration. The results that are given by the expert system include the whole architecture of PPC software package that is applicable in a certain manufacturing setting.KeywordsProduction SystemExpert SystemSemantic NetworkCustomer OrderSoftware FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Recent research in knowledge‐based expert systems of VLSI design tools has concentrated on placement, routing, and cell generation. This paper presents an alternative application for artificial intelligence (AI) techniques on compaction design for a VLSI mask layout‐expert compactor. In order to overcome the shortcomings of iterative search through a large problem space within a working memory, and therefore, to speed‐up the runtime of compaction, a set of rule‐based region query operations and knowledge‐based techniques for the plane sweep method are proposed in this system. Experimental results have explored the possibility of using expert system technology (EST) to automate the compaction process by “reasoning” out the layout design and applying sophisticated expert rules to its knowledge base.

The scope and limitations of first generation expert systems

An optimized design of a matrix treatment involves fluid selection and acidizing simulations to predict the outcome of the treatment; Many matrix acidizing treatment designs and fluid selections have been successfully accomplished by utilizing expert system technology. However, none of these present a complete and optimized result (i.e., by utilizing the output of the expert system to predict the acidizing outcome using an acidizing numerical simulator). In the meantime, several acidizing computer simulation studies have been conducted separately. This paper presents a study which integrates the treatment design, particularly the fluid selection process, and acidizing simulation for sandstone formations. Required parameters for sandstone acidizing such as acid type, concentration, volume, and injection rate/pressure are first selected using an expert system. The output from the expert system is further used for the input to an acidizing numerical simulator (UTACID). A new sandstone acidizing reaction model, appropriate for a high-temperature environment, and anisotropic medium have been implemented into UTACID to enhance the performance of the simulator. The expert system and the simulator have been integrated to provide an optimization tool for sandstone acidizing treatment design and simulation.

An important aspect of the production of quality software is that of software assessment and certification. This involves the measurement of a finished software product using a set of recognised Software Assessment Units. It is essential that at the specification of a piece of software a set of relevant software attributes with a suitable subset of Assessment Units is identified. The set is used to determine whether or not the finished product meets an appropriate quality standard. It has been recognised that a Software Assessment Advisor (SAA) is a necessary tool to assist with this task. An SAA can help in determining the software attributes which should be measured and in selecting an appropriate set of Assessment Units. The SAA will use information concerning the categorisation and functions of a proposed software product, the user's requirements, available tools, and prior assessment experiences in these tasks. So far, no clear approach has been identified for the development of an SAA. The investigation of the modelling required by an SAA indicates that Expert Systems techniques could provide a useful step forward in this development. Expert Systems techniques are able to relate the information required by the certification procedure intelligently by means of the knowledge model and to support symbolic reasoning to advise the software certifier. Also an Expert System's capability for knowledge learning is significant with respect to the relatively young area of Software Assessment and Certification. This paper discusses the activities involved in the Assessment and Certification process, describes how an SAA can assist for the process and how an Expert Systems approach has been used in the development of a prototype SAA.

Expert systems use the expertise of experienced people to evaluate and solve problems in a decision making domain. Such systems use their knowledge base to model a human approach to the problems' solution. Expert system technology has been applied to many areas of problem solving. This paper describes an expert system approach to completing partially routed printed circuits boards. An experimental expert system has been implemented to solve the associated local blockage problem. This research should provide the foundation for the development of an expert system for a Computer Aided Design Workstation.

Expert systems (ES) are computer programs which make recommendations and draw conclusions from heuristic rules and from relationships derived out of human experience. A decision support system (DSS) is a computer-based management information system (MIS) linking one or more data bases to computer software for exploration and analysis of the data. Combined in a single system an ES and a DSS comprise an expert support system. A simple expert support system for use in fishery stock assessment and management is described. It demonstrates a number of practical benefits of the use of expert systems technology to implement and integrate some simple tasks, i.e., bioeconomic modelling, cost/benefit sampling analyses, and technical interpretation, namely: (1) the effects of different population structures and patterns of fishing on modelling results can be examined under the programmed guidance of the expert system; (2) the effects of different levels of sampling precision on modelling results can be similarly examined; (3) cost/benefit analyses of different sampling budgets can be examined; and (4) management advice specific to each sampling, surplus production (four different models), and net present value (NPV) case is given. An examination of the future of expert support systems in fishery stock assessment and management considers four possible levels of support, as follows: (1) to provide technical explanations, (2) to manage and catalog repetitive processes, (3) to guide “low-level decisions”, and (4) to contribute directly to strategic and policy decisions. Currently, movement in expert systems usage is toward so-called “embedded systems”, for example, an expert support system which comprises, in addition to computational models and databases, one or more expert systems. Problems related to the design and development of ESS for fishery stock assessment and management are challenging conceptual problems; the construction and programming of ESS are reasonably tractable tasks. Determining what an ESS will do is often the principle conceptual question. Expert support systems provide another tool for fishery stock assessment scientists and managers to increase their productivity.

Expert systems in wind engineering

Expert systems: A short introduction

The medical and technology fields are rapidly advancing, leading many people to use computers to help diagnose, prevent, and treat human diseases. One major issue in the medical world is the imbalance between the number of patients and doctors. Additionally, most people lack medical training, so when experiencing symptoms of a disease, it is often difficult to immediately know the correct steps to take. Eye diseases vary in severity, ranging from mild to severe. One common eye disorder affecting many people is refractive error, which generally falls into two categories: hyperopia (farsightedness) and myopia (nearsightedness). Early detection of symptoms related to refractive errors requires accurate and prompt diagnosis. Therefore, with the rapid development of technology, it is essential to develop systems capable of early detection of eye diseases, especially refractive errors, by using technology that mimics human expert capabilities, such as expert systems. This expert system integrates expert knowledge within two main environments: the development environment and the consultation environment, helping the community diagnose diseases more easily and efficiently. For example, the use of the Certainty Factor method in expert systems enables the calculation of diagnostic certainty levels based on the combination of symptoms reported by patients and expert knowledge, achieving a confidence level of up to 96.7%. This demonstrates that expert system technology can be a valuable tool in addressing the imbalance between patients and doctors while improving access to faster and more accurate diagnoses. To build such systems, Microsoft Visual Studio .NET provides a complete set of tools for developing ASP.NET web applications, XML Web Services, desktop applications, and mobile applications. Within Visual Studio, .NET programming languages such as Visual Basic, Visual C++, Visual C# (CSharp), and Visual J# (JSharp) are used in a unified integrated development environment (IDE), enabling developers to efficiently share tools and resources to create reliable and user-friendly expert system applications. The system was developed and tested using symptom data from 40 patients collected at the Tanjung Sarang Elang Community Health Center. The testing showed a diagnostic accuracy of up to 96.7% in detecting symptoms of both hyperopia and myopia.

In January 1987, the University of Karlsruhe and Siemens started a joint research project on expert system application for supporting digital switch maintenance. Major results of the first project phase are summarized. Maintenance phases supported by an expert system are defined. Expert system technology is used to help technicians to handle alarms of the Siemens digital switching system EWSD correctly and quickly. The implemented and successfully tested expert system prototype deals with coordination processor alarms in EWSD exchanges. >