Expert system aid for design of overload line distribution networks

The use of expert system techniques in power distribution system design is examined. The selection and siting of equipment on overhead line networks is chosen for investigation as the use of equipment such as auto-reclosers, etc., represents a substantial investment and has a significant effect on the reliability of the system. Through past experience with both equipment and network operations, most decisions in selection and siting of this equipment are made intuitively, following certain general guidelines or rules of thumb. This heuristic nature of the problem lends itself to solution using an expert system approach. A prototype has been developed and is currently under evaluation in the industry. Results so far have demonstrated both the feasibility and benefits of the expert system as a design aid.

Real-time expert system for fault location on high voltage underground distribution cables

To ensure minimum loss of system security and revenue it is essential that faults on underground cable systems be located and repaired rapidly. Currently in the UK, the impulse current method is used to prelocate faults, prior to using acoustic methods to pinpoint the fault location. The impulse current method is heavily dependent on the engineer's knowledge and experience in recognising/interpreting the transient waveforms produced by the fault. The development of a prototype real-time expert system aid for the prelocation of cable faults is described. Results from the prototype demonstrate the feasibility and benefits of the expert system as an aid for the diagnosis and location of faults on underground cable systems.

Power distribution network design aided by an expert system

The next couple of years will see the need for replacement of a large amount of life-expired switchgear on the UK 11 kV distribution system. Latest technology and alternative equipment have made the choice of replacement a complex task. The authors present an expert system as an aid to the decision process for the design of the 11 kV power distribution network.

Expert system to calculate the cofficient of friction : an approach to enhance traffic safety

Friction plays a key role in causing slipperiness as a low coefficient of friction on the road may result in slippery and hazardous conditions. Analyzing the strong relation between friction and accident risk on winter roads is a difficult task. Many weather forecasting organizations use a variety of standard and bespoke methods to predict the coefficient of friction on roads. This article proposes an approach to predict the extent of slipperiness by building and testing an expert system. It estimates the coefficient of friction on winter roads in the province of Dalarna, Sweden using the prevailing weather conditions as a basis. Weather data from the road weather information system, Sweden (RWIS) was used. The focus of the project was to use the expert system as a part of a major project in VITSA, within the domain of intelligent transport systems

An extended query-the-user tool for expert systems

An extended version of HIER, a query-the-user facility for expert systems is presented. HIER was developed to run over Prolog programs, and has been incorporated to systems that support the design of large and complex applications. The framework of the extended version is described,; as well as the major features of the implementation. An example is included to illustrate the use of the tool, involving the design of a specific database application.

Expert system for hot forging design

Planning hot forging processes is a time-consuming activity with high costs involved because of the trial-and-error iterative methods used to design dies and to choose equipment and process conditions. Some processes demand many months to produce forged parts with controlled shapes, dimensions and microstructure. This paper shows how expert systems can help engineers to reduce the time needed to design precision forged parts and dies from machined parts. The software ADHFD interfacing MS Visual Basic v.5.0 and SolidEdge v.3.0 was used to design flashless hot forged gears, chosen from families of gears. © 1998 Elsevier Science S.A. All rights reserved.

Cogeneration design problem Computational complexity analysis and solution through an expert system

Purpose - The purpose of this paper is twofold: to analyze the computational complexity of the cogeneration design problem; to present an expert system to solve the proposed problem, comparing such an approach with the traditional searching methods available.Design/methodology/approach - The complexity of the cogeneration problem is analyzed through the transformation of the well-known knapsack problem. Both problems are formulated as decision problems and it is proven that the cogeneration problem is np-complete. Thus, several searching approaches, such as population heuristics and dynamic programming, could be used to solve the problem. Alternatively, a knowledge-based approach is proposed by presenting an expert system and its knowledge representation scheme.Findings - The expert system is executed considering two case-studies. First, a cogeneration plant should meet power, steam, chilled water and hot water demands. The expert system presented two different solutions based on high complexity thermodynamic cycles. In the second case-study the plant should meet just power and steam demands. The system presents three different solutions, and one of them was never considered before by our consultant expert.Originality/value - The expert system approach is not a "blind" method, i.e. it generates solutions based on actual engineering knowledge instead of the searching strategies from traditional methods. It means that the system is able to explain its choices, making available the design rationale for each solution. This is the main advantage of the expert system approach over the traditional search methods. On the other hand, the expert system quite likely does not provide an actual optimal solution. All it can provide is one or more acceptable solutions.

Novel expert system for defining power quality compensators

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Expert system to detect incipient fauts in rotating machines using fuzzy logic and virtual instrumentation

This paper refers to the design of an expert system that captures a waveform through the use of an accelerometer, processes the signal and converts it to the frequency domain using a Fast Fourier Transformer to then, using artificial intelligence techniques, specifically Fuzzy Reasoning, it determines if there is any failure present in the underlying mode of the equipment, such as imbalance, misalignment or bearing defects.

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

Abstract Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.

Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads

The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and linguistic variables, measurable meteorological and topographical factors and observable characteristics of the snow cover. Output of the inference method is the quantified release disposition for wet snow avalanches. Combining topographical parameters and the spatial interpolation of the calculated release disposition a hazard index map is dynamically generated. Furthermore, the spatial and temporal variability of damage potential on roads exposed to wet snow avalanches can be quantified, expressed by the number of persons at risk. The application of the rule base to the available data in the study area generated plausible results. The study demonstrates the potential for the application of expert systems and fuzzy logic in the field of natural hazard monitoring and risk management.

Hybrid Approach Combining Machine Learning and a Rule-Based Expert System for Text Categorization

This paper discusses a novel hybrid approach for text categorization that combines a machine learning algorithm, which provides a base model trained with a labeled corpus, with a rule-based expert system, which is used to improve the results provided by the previous classifier, by filtering false positives and dealing with false negatives. The main advantage is that the system can be easily fine-tuned by adding specific rules for those noisy or conflicting categories that have not been successfully trained. We also describe an implementation based on k-Nearest Neighbor and a simple rule language to express lists of positive, negative and relevant (multiword) terms appearing in the input text. The system is evaluated in several scenarios, including the popular Reuters-21578 news corpus for comparison to other approaches, and categorization using IPTC metadata, EUROVOC thesaurus and others. Results show that this approach achieves a precision that is comparable to top ranked methods, with the added value that it does not require a demanding human expert workload to train

QL-CONST1: an expert system for quality level prediction in concrete structures

Current trends in the fields of artifical intelligence and expert systems are moving towards the exciting possibility of reproducing and simulating human expertise and expert behaviour into a knowledge base, coupled with an appropriate, partially ‘intelligent’, computer code. This paper deals with the quality level prediction in concrete structures using the helpful assistance of an expert system, QL-CONST1, which is able to reason about this specific field of structural engineering. Evidence, hypotheses and factors related to this human knowledge field have been codified into a knowledge base. This knowledge base has been prepared in terms of probabilities of the presence of either hypotheses or evidence and the conditional presence of both. Human experts in the fields of structural engineering and the safety of structures gave their invaluable knowledge and assistance to the construction of the knowledge base. Some illustrative examples for, the validation of the expert system behaviour are included.

Computational Fluid Dynamics Expert System using Artificial Neural Networks

The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations. As a results of this, Computational Fluid Dynamic (CFD) solvers are widely used in the aeronautical field. These solvers require the correct selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on the proper choice of these parameters. In this paper we create an expert system capable of making an accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver. Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time required for the convergence of a CFD solver.