Abstract: This volume focuses on model reduction problems with particular applications in electrical engineering. Starting with a clear outline of the technique and its wide methodological background, central topics are introduced including mathematical tools, physical processes, numerical computing experience, software developments and knowledge of system theory. Several model reduction algorithms are then discussed. The aim of this work is to give the reader an overview of reduced-order model design and an operative guide. Particular attention is given to providing basic concepts for building expert systems for model reduction.

Abstract: Experts, who were the sole active dispensers of certain kinds of knowledge in the days before A1, have now often assumed a rather passive role. They relay their knowledge to various novices, knowledge engineers, experimental psychologists or cognitivists - or other experts - involved in the development and understanding of expert systems. This book aims to achieve a balance between experimentalists and theoreticians who deal with expertise. It tries to establish the benefits to society of an advanced technology for representing and disseminating the knowledge and skills of the best corporate managers, the most seasoned pilots, and the most renowned medical diagnosticians. This book interests psychologists as well as all those out in the trenches developing expert systems, and everyone pondering the nature of expertise and the question of how it can be studied scientifically.

Abstract: There is a common problem in artificial intelligence (AI) and information security. In AI, an expert system needs to be able to justify and explain a decision to the user. In information security, experts need to be able to explain to the public why a system is secure. In both cases, an important goal of explanation is to acquire or maintain the users' trust. In this paper, I investigate the relation between explanation and trust in the context of computing science. This analysis draws on literature study and concept analysis, using elements from system theory as well as actor-network theory. I apply the conceptual framework to both AI and information security, and show the benefit of the framework for both fields by means of examples. The main focus is on expert systems (AI) and electronic voting systems (security). Finally, I discuss consequences of the analysis for ethics in terms of (un)informed consent and dissent, and the associated division of responsibilities.

Abstract: The article proposes an expert system for detection, and subsequent investigation, of groups of collaborating automobile insurance fraudsters. The system is described and examined in great detail, several technical difficulties in detecting fraud are also considered, for it to be applicable in practice. Opposed to many other approaches, the system uses networks for representation of data. Networks are the most natural representation of such a relational domain, allowing formulation and analysis of complex relations between entities. Fraudulent entities are found by employing a novel assessment algorithm, Iterative Assessment Algorithm (IAA), also presented in the article. Besides intrinsic attributes of entities, the algorithm explores also the relations between entities. The prototype was evaluated and rigorously analyzed on real world data. Results show that automobile insurance fraud can be efficiently detected with the proposed system and that appropriate data representation is vital.

Abstract: The hotel industry is one of the leading stakeholders in the tourism sector. In order to reduce the traveler's cost of seeking accommodations, enforce the return ratio efficiency of guest rooms and enhance total operating performance, evaluating and selecting a suitable hotel location has become one of the most critical issues for the hotel industry. In this scenario, recommender services are increasingly emerging which employ intelligent agents and artificial intelligence to ''cut through'' unlimited information and obtain personalized solutions. Taking this assumption into account, this paper presents Sem-Fit, a semantic hotel recommendation expert system, based on the consumer's experience about recommendation provided by the system. Sem-Fit uses the consumer's experience point of view in order to apply fuzzy logic techniques to relating customer and hotel characteristics, represented by means of domain ontologies and affect grids. After receiving a recommendation, the customer provides a valuation about the recommendation generated by the system. Based on these valuations, the rules of the system are updated in order to adjust the new recommendations to past user experiences. To test the validity of Sem-Fit, the validation accomplished includes the interaction of the customer with the system and then the results are compared with the expert recommendation for each customer profile. Moreover, the values of precision and recall and F1 have been calculated, based on three points of view, to measure the degree of relevance of the recommendations of the fuzzy system, showing that the system recommendations are on the same level as an expert in the domain.

Abstract: The objective of this work is to develop techniques to automate the condition-based maintenance procedure. It is observed that vibration signals are capable of alarming the malfunctions in machineries. In order to overcome the shortcomings in the traditional vibration analysis using time-domain and frequency-domain features, two new approaches based on wavelet transform, artificial neural network and fuzzy rules are proposed for detecting and localizing defects in rolling element bearings. The two expert systems are developed and tested with the use of vibration signals collected from the bearing housing of an experimental setup. Experiment results show that the proposed approaches are sensitive and reliable in detecting defects on the outer race, inner race and rolling elements of bearings. The proposed approaches may be used for other fault diagnoses such as gear faults, coupling faults, belts in industries. It is also expected from the obtained results that the generalized defect detection will be easi...

Abstract: This review aims to discuss expert systems in general and how they may be used in medicine as a whole and clinical microbiology in particular (with the aid of interpretive reading). It considers rule-based systems, pattern-based systems, and data mining and introduces neural nets. A variety of noncommercial systems is described, and the central role played by the EUCAST is stressed. The need for expert rules in the environment of reset EUCAST breakpoints is also questioned. Commercial automated systems with on-board expert systems are considered, with emphasis being placed on the "big three": Vitek 2, BD Phoenix, and MicroScan. By necessity and in places, the review becomes a general review of automated system performances for the detection of specific resistance mechanisms rather than focusing solely on expert systems. Published performance evaluations of each system are drawn together and commented on critically.

Abstract: Digital Signal Processing in Power System Protection and Control bridges the gap between the theory of protection and control and the practical applications of protection equipment. Understanding how protection functions is crucial not only for equipment developers and manufacturers, but also for their users who need to install, set and operate the protection devices in an appropriate manner.After introductory chapters related to protection technology and functions, Digital Signal Processing in Power System Protection and Control presents the digital algorithms for signal filtering, followed by measurement algorithms of the most commonly-used protection criteria values and decision-making methods in protective relays. A large part of the book is devoted to the basic theory and applications of artificial intelligence techniques for protection and control. Fuzzy logic based schemes, artificial neural networks, expert systems and genetic algorithms with their advantages and drawbacks are discussed. AI techniques are compared and it is also shown how they can be combined to eliminate the disadvantages and magnify the useful features of particular techniques.The information provided in Digital Signal Processing in Power System Protection and Control can be useful for protection engineers working in utilities at various levels of the electricity network, as well as for students of electrical engineering, especially electrical power engineering. It may also be helpful for other readers who want to get acquainted with and to apply the filtering, measuring and decision-making algorithms for purposes other than protection and control, everywhere fast and on-line signal analysis is needed for proper functioning of the apparatus.

Abstract: This paper presents an expert system for sorting open and closed shell pistachio nuts. A prototype was set up to detect closed shell pistachio nuts by dropping them onto a steel plate and recording the acoustic signals that was generated when a kernel hit the plate. To determine the important characteristics and to unravel the significance of these signals, further analysis or processing was required. J48 decision tree (DT) is used for both feature selection and classification. Initially, the J48 DT was used for selecting the best statistical features that will discriminate among two classes from impact acoustic signals. The output of J48 DT algorithm was then converted into crisp IF-THEN rules and membership function sets of the fuzzy classifier. Four IF-THEN rules, generated from the extracted features of J48 DT, were required by the fuzzy classifier. To evaluate the performance of the expert system, data on 300 nuts of open and closed shells were used. The data were initially divided into two parts: 210 instances (70%) for training and the remaining 90 instances (30%) for testing the classifier. The correct classification rate and RMSE for the training set were 99.52% and 0.07, and for the test set were 95.56% and 0.21, respectively. These encouraging results as well as the robustness of the FIS based expert system makes the approach ideal for automated inspection systems.

Abstract: Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. This paper presents an overview of the past and current practice in long- term demand forecasting. It introduces methods, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules, support vector machines, wavelet networks and expert systems.

Abstract: Research highlights? An expert system is developed based on wavelet transform and radon neural network (WRNN) for pavement distress classification ? Classification is performed using the neural network ? Feature extracted from wavelet decomposition and radon transform ? The wavelet decomposition and the radon transform have been demonstrated to be an effective tool for feature selecting for neural network Nowadays, pavement distresses classification becomes more important, as the computational power increases Recently, multi-resolution analysis such as wavelet decompositions provides very good multi-resolution analytical tools for different scales of pavement analysis and distresses classification In this paper an expert system is proposed for pavement distress classification A radon neural network, based on wavelet transform expert system is used for increasing the effectiveness of the scale invariant feature extraction algorithm Wavelet modulus is calculated and Radon transform is then applied to the wavelet modulus The features and parameters of the peaks are finally used for training and testing the neural network Experimental results demonstrate that the proposed expert system is an effective method for pavement distress classification The test performances of this study show the advantages of proposed expert system: it is rapid, easy to operate, and have simple structure

Abstract: 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.

Abstract: The four-volume set LNAI 6881-LNAI 6884 constitutes the refereed proceedings of the 15th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2011, held in Kaiserslautern, Germany, in September 2011. Part 1: The total of 244 high-quality papers presented were carefully reviewed and selected from numerous submissions. The 61 papers of Part 1 are organized in topical sections on artificial neural networks, connectionists systems and evolutionary computation, machine learning and classical AI, agent, multi-agentsystems, knowledge based and expert systems, intelligent vision, image processing and signal processing, knowledge management, ontologies, and data mining.

Abstract: There are generally two approaches to the design of a neural fuzzy system: (1) design by human experts, and (2) design through a self-organization of the numerical training data. While the former approach is highly subjective, the latter is commonly plagued by one or more of the following major problems: (1) an inconsistent rulebase; (2) the need for prior knowledge such as the number of clusters to be computed; (3) heuristically designed knowledge acquisition methodologies; and (4) the stability-plasticity tradeoff of the system. This paper presents a novel self-organizing neural fuzzy system, named Self-Adaptive Fuzzy Inference Network (SaFIN), to address the aforementioned deficiencies. The proposed SaFIN model employs a new clustering technique referred to as categorical learning-induced partitioning (CLIP), which draws inspiration from the behavioral category learning process demonstrated by humans. By employing the one-pass CLIP, SaFIN is able to incorporate new clusters in each input-output dimension when the existing clusters are not able to give a satisfactory representation of the incoming training data. This not only avoids the need for prior knowledge regarding the number of clusters needed for each input-output dimension, but also allows SaFIN the flexibility to incorporate new knowledge with old knowledge in the system. In addition, the self-automated rule formation mechanism proposed within SaFIN ensures that it obtains a consistent resultant rulebase. Subsequently, the proposed SaFIN model is employed in a series of benchmark simulations to demonstrate its efficiency as a self-organizing neural fuzzy system, and excellent performances have been achieved.

Abstract: One of the critical issues in data mining process especially for organizations is evaluating the success level of performed data mining projects. The purpose of this research is designing a Fuzzy expert system for the evaluation of success level of data mining projects based on quality of CRISP-DM methodology phases as one of the famous data mining methodologies. Here the CRISP-DM phases are specified as inputs of Fuzzy Inference System (FIS) model and the output is the success level of data mining project. This system has been designed by MATLAB software and has been implemented for a data mining project in an Iranian Bank as empirical study.

Abstract: An efficient expert system for the power transformer condition assessment is presented in this paper. Through the application of Duval's triangle and the method of the gas ratios a first assessment of the transformer condition is obtained in the form of a dissolved gas analysis (DGA) diagnosis according IEC 60599. As a second step, a knowledge mining procedure is performed, by conducting surveys whose results are fed into a first Type-2 Fuzzy Logic System (T2-FLS), in order to initially evaluate the condition of the equipment taking only the results of dissolved gas analysis into account. The output of this first T2-FLS is used as the input of a second T2-FLS, which additionally weighs up the condition of the paper-oil system. The output of this last T2-FLS is given in terms of words easily understandable by the maintenance personnel. The proposed assessing methodology has been validated for several cases of transformers in service.

Abstract: From their first applications until now, expert systems have provided solutions to multiple problems in companies of all types. With the advent of the Internet and its evolution, web-based expert systems have become very important. Moreover, the arrival of new mobile devices that can connect to the Internet has made it easy to access information from any place at any time, creating new requirements for web systems. The creation of an expert system normally requires certain technical knowledge and concepts of artificial intelligence (AI). If the need to make it accessible through the Internet is added, the degree of technical knowledge necessary for its development is greater, entailing an unaffordable cost for small and medium-sized companies. In this article, we present a tool for the development of web-based expert systems which permits the expert to define the knowledge without having to know anything about AI. The proposed inference engine is integrated in a web server through which it can be accessed from any device with an Internet connection. Finally, the article presents examples of developments achieved via the proposed framework.

Abstract: The invention discloses a remote monitoring and intelligent control system and method of an agricultural greenhouse based on an M2M framework. The system comprises an information acquisition module, an M2M network transmission module, a service monitoring module, an expert decision module, a system display module and a remote control module. The method comprises: firstly, deploying a plurality of environmental parameter sensors in a glasshouse to collect key information; then, in an M2M mode, sending the key information to a centered platform via the Internet or the 3G network; storing, displaying and analyzing data in the centered platform; combining with a crop model and an expert system to output decision information; and issuing a control order via the Internet or the 3G network. The system can avoid the limitation of the local automatic control system of the common greenhouse and realizes the situation that the production in a plurality of greenhouses is supported on one platform, thus the system has good expansibility and supports scale application. The system provides the means for checking systems and issuing orders on the Internet and a mobile terminal, is convenient and can transmit information by the broadband Internet and the 3G communication network.

Abstract: In this paper a recursive algorithm based on the Bayesian reasoning approach is proposed to update a belief rule based (BRB) expert system for pipeline leak detection and leak size estimation. In addition to using available real time data, expert knowledge on the relationships of the parameters among different rules is incorporated into the updating process so that the performance of the expert system can be improved. Experiments are carried out to compare the newly proposed algorithm with the previously published algorithms, and results show that the proposed algorithm can update the BRB expert system faster and more accurately, which is important for real-time applications. The BRB expert systems can be automatically tuned to represent complex real world systems, and applied widely in engineering.

Abstract: The detection of non-technical losses (NTLs), in most papers, commonly deals with the utilization of the registered consumption for each customer; besides, some researchers used the economic activity, the active/reactive ratio and the contract power. Currently, utility company databases store enormous amounts of information on both installations and customers: consumption, technical information on the measure equipment, documentation, inspections results, commentaries of inspectors, etc. In this paper, an integrated expert system (IES) for the analysis and classification of all the available useful information of the customer is presented. Customer classification identifies the presence of an NTL and the problem type. This IES include several modules: text mining module for analysis of inspector commentaries and extraction of additional information on the customer, data mining module to draw up the rules that determine the customer estimate consumption, and the Rule Based Expert System module to analyze each customer using the results of the text and data mining modules. This IES is used with real data extracted from Endesa company databases. Endesa is the most important power distribution company in Spain, and one of the most significant companies of Europe. This IES is used in the test phase by human experts in the Endesa company. In this phase, the IES is used as a Decision Support System (DSS), as it contains another module which provides a report with additional information about the customer and a summarized result that the inspectors can use to reach a decision.

Abstract: Decision support through information technology become a part of our everyday lives. In this paper we produce a Fuzzy Expert System (FES) to diagnosis of back pain disease based on the clinical observation symptoms using fuzzy rules. The clinical observation symptoms which processed by fuzzy expert system may be used fuzzy concepts to describe that symptoms such as (little, medium, high). To deal with fuzzy concepts in clinical observation symptoms we should be used fuzzy rules to hold this concepts. The parameters used as input for this fuzzy expert system were Body Mass Index (BMI), age, and gender of patient as well as the clinical observation symptoms. The proposed expert system can help to diagnosis of back pain disease and produce medical advice to the patient. The system implemented and tested using clinical data that is correspond to 20 patients with different back pain diseases. The proposed system implemented using Visual Prolog programming language ver. 7.1.

Abstract: Purpose – The purpose of this paper is to examine the usefulness of a heuristic expert system, to show its applicability to real‐world valuation problems, and to suggest several avenues for statistical testing.Design/methodology/approach – The expert systems follow a traditional sales adjustment grid format, with sufficient data for non‐parametric testing.Findings – The paper finds that, while non‐parametric statistics provide weaker results than traditional (e.g. hedonic regression) modeling, the technique provides a statistically testable model useful in situations with limited data and/or poorly characterized probability functions.Practical implications – This paper addresses the conundrum faced by real estate valuers on the lack of statistical underpinnings of traditional heuristic models.Originality/value – This is one of the first empirical studies in the valuation literature exploring statistical characterization of heuristic valuation methods.