Components Of Expert System Research Articles

Components of Oranta-AO software expert system for innovative application of blood pressure monitors.

The authors developed and substantiated the original methods of arterial oscillography, which were implemented in the developed Oranta-AO information system. The methods of application to the arterial oscillogram registered at measurement of arterial pressure gives the possibility to carry out the supplementary systematic assessment of health, functional state of cardiovascular system, its reserve possibilities etc. The authors also developed an Expert System (based on machine-learning methods) for the differential diagnosis of risks of heart, lung, mental illness and prognosis of some blood parameters. Oranta-AO software system was created based on research results due to methods and algorithms that were innovate. For the mathematical modeling of arterial oscillograms used cyclic random processes. Methods of arterial oscillograms processing based on its model in the form of a cyclic random process was developed. The method of evaluation of the rhythm function of arterial oscillograms and statistical methods for estimating the probabilistic characteristics of arterial oscillograms were developed. To solve the clustering problem, the Python k-means and k-means++ algorithm were used. Oranta-AO information system consists of three interrelated parts: mobile application, computing kernel and web system. Computing kernel and web system are deployed on AWS servers and have been tested already. The developed environment aims to be integrated into every new model of electronic meters in the world. Certification (EN 62304:2014, ISO 13485: 2018) in Ukraine is completed, PCT priority is completed. The next step will be to establish cooperation with manufacturers of electronic pressure monitors, patenting and certification in world.

Business analytics in Industry 4.0: A systematic review

AbstractRecently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet‐of‐Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data‐based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Artificial intelligence-based solutions and applications have great potential in various fields of electrical power engineering. The problem of the electrical reliability of power equipment directly refers to the immunity of high-voltage (HV) insulation systems to operating stresses, overvoltages and other stresses—in particular, those involving strong electric fields. Therefore, tracing material degradation processes in insulation systems requires dedicated diagnostics; one of the most reliable quality indicators of high-voltage insulation systems is partial discharge (PD) measurement. In this paper, an example of the application of a neural network to partial discharge images is presented, which is based on the convolutional neural network (CNN) architecture, and used to recognize the stages of the aging of high-voltage electrical insulation based on PD images. Partial discharge images refer to phase-resolved patterns revealing various discharge stages and forms. The test specimens were aged under high electric stress, and the measurement results were saved continuously within a predefined time period. The four distinguishable classes of the electrical insulation degradation process were defined, mimicking the changes that occurred within the electrical insulation in the specimens (i.e., start, middle, end and noise/disturbance), with the goal of properly recognizing these stages in the untrained image samples. The results reflect the exemplary performance of the CNN and its resilience to manipulations of the network architecture and values of the hyperparameters. Convolutional neural networks seem to be a promising component of future autonomous PD expert systems.

Learning competitive channel-wise attention in residual network with masked regularization and signal boosting

Image classification is an essential component of expert and intelligent systems. The accuracy and efficiency of image classification algorithms significantly affect the performance of related expert systems. Residual network (ResNet) shows strong superiority in image modeling. However, it has also been proved to be low-efficient. In this study, we proposed a novel channel-wise attention mechanism to alleviate the redundancy of ResNet. We introduce the identity mappings into the scope of channel relationship modeling. In this way, the identity mapping can join the optimized process of self-supplementary modeling. Besides, we present the masked regularization for squeezed signals and enhance the robustness of channel-relation encoding. Finally, we verify the performance of the proposed method. The experiments are carried out on the datasets CIFAR-10, CIFAR-100, SVHN, and ImageNet. The proposed method effectively improves the performance of image classification-related expert systems. Moreover, our approach is hot-swappable, has broad applicability, so it has great practical significance for experts and intelligent systems.

Analysıs of Stages of Development, Current State and Prospects of the Expert Systems

The objects of the study are stages of development and modern state. In general terms, expert systems are knowledge-based systems. This paper focuses on the components and principles of expert systems. Expert systems are also described. The components of expert systems include knowledge base, logical impact mechanism, user interface and decision-making. In addition, this article describes the capabilities of expert systems. One challenge is to identify the future prospects of expert systems. The research examined the expert system and its significance. It also focuses on generations of expert systems. The first generation of expert systems includes systems created before 1990. This article discusses SAINT, DENDRAL and HEARSAY-1. The features of this expert systems are also discussed here. First-generation expert systems are research prototypes. As a result, the foundations of artificial intelligence were developed. Mostly first-generation expert systems were used as a passive assistant expert. The second generation of expert systems refers to systems created since 1990. Features of second-generation expert systems include dynamism, interactivity, and processing of disparate knowledge. Unlike first-generation expert systems, these systems are able to test the completeness of the knowledge base, to process fuzzy knowledge. Their main difference is the ability to integrate second-generation expert systems with existing systems. At the moment, statistical and dynamic expert systems are distinguished. This article describes the current status of both types. Here are also discussed the tools of statistical and dynamic expert systems. At the end, possible prospects of expert systems are received.

REVIEW OF METHODOLOGIES AND APPLICATIONS OF EXPERT SYSTEMS: OPTIMIZATION PERSPECTIVE

A comprehensive study of research carried out by eminent researchers givesa critical analysis of concepts, methodologies and applications used for their research work. It gives context for the continuation of ongoing research or inspires new research ideas.In general, a literature evaluation aids in the identification of research gaps. This research paper presents a systematic study of components, methodologies and applications of various expert systems introduced in the diverse range of sectors. This paper surveys the development of expert systems through the study of various research articles related to the expert system methodologies, applications, and components that include knowledge acquisition, knowledge representation schemes, inference engines, learning tools etc.

REVIEW OF METHODOLOGIES AND APPLICATIONS OF EXPERT SYSTEMS: OPTIMIZATION PERSPECTIVE

A comprehensive study of research carried out by eminent researchers givesa critical analysis of concepts, methodologies and applications used for their research work. It gives context for the continuation of ongoing research or inspires new research ideas.In general, a literature evaluation aids in the identification of research gaps. This research paper presents a systematic study of components, methodologies and applications of various expert systems introduced in the diverse range of sectors. This paper surveys the development of expert systems through the study of various research articles related to the expert system methodologies, applications, and components that include knowledge acquisition, knowledge representation schemes, inference engines, learning tools etc.

Building an expert system for diagnosing traction electric motors of rolling stock

This article is devoted to the development of an expert system for diagnosing traction electric motors of rolling stock. The article describes the process of selecting diagnostic parameters, allowing to assess the quality of machining and assembly of the traction motor. The process of formation of a fuzzy model of diagnosing a traction motor based on the Takagi-Sugeno algorithm is shown. Selected diagnostic parameters are taken as input linguistic variables of a fuzzy model. As an output linguistic variable, a complex indicator of the quality of mechanical processing of the collector of a traction motor was adopted. The article presents the membership functions for the input and output linguistic variables and the rules for fuzzy products for the model being implemented. The hypersurface of the output linguistic variable in the space of various attributes is shown. Based on a fuzzy model, a neural fuzzy network has been developed, which allows to evaluate the quality of mechanical processing of a collector of a traction motor, and presents the results of its training. The developed neural fuzzy network is recommended to be used as one of the components of a comprehensive expert system for diagnosing traction electric motors of rolling stock.

FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS

In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid expert systems. The approaches to the analysis of natural language sentences, which are used for the work of hybrid expert systems with artificial neural networks, are considered. A bulletin board is shown, its structure and principle of operation are described. The structure of the bulletin board is divided into levels and sublevels. At sublevels, a confidence factor is applied. The dependence of the values of the confidence factor on the fulfillment of a particular condition is shown. The links between the levels and sublevels of the bulletin board are also described. As an artificial neural network architecture, the «key-threshold» model is used, the rule of neuron operation is shown. In addition, an artificial neural network has the property of training, based on the application of the penalty property, which is able to calculate depending on the accident situation. The behavior of a complex technical system, as well as its faulty states, are modeled using a model that describes the structure and behavior of a given system. To optimize the data of a complex technical system, an evolutionary algorithm is used to minimize the objective function. Solutions to the optimization problem consist of Pareto solution vectors. Optimization and training tasks are solved by using the Hopfield network. In general, a hybrid expert system is described using semantic networks, which consist of vertices and edges. The reference model of a complex technical system is stored in the knowledge base and updated during the acquisition of new knowledge. In an emergency, or about its premise, with the help of neural networks, a search is made for the cause and the control action necessary to eliminate the accident. The considered approaches, interacting with each other, can improve the operation of diagnostic artificial neural networks in the case of emergency management, showing more accurate data in a short time. In addition, the use of such a network for analyzing the state of health, as well as forecasting based on diagnostic data using the example of a complex technical system, is presented.

Diagnostic methods and risk analysis based on fuzzy soft information

In our daily life problem we face uncertainties in making right decisions. In this study, we propose two different decision-making problems in medical field. The first problem is fever diagnosing and second problem is mouth cancer risk analysis. In the first problem, we use fuzzy soft similarity measures and fuzzy soft matrix operations to diagnose the type of fever. We consider a hypothetical case study and manipulate similarity measures on it. Our work diagnoses different patients having similar symptoms. We also develop a small application using JAVA. In the second problem, we perform risk analysis of mouth cancer. The proposed fuzzy soft expert system takes two biochemical parameters as inputs that is, serum total malondialdehyde (MDA), and serum proton donors capacity (donors_protons) and determines the risk of mouth cancer. Our study facilitates doctors by diagnosing mouth cancer at its earlier stages. There are four main components of our fuzzy soft expert system. The first component is named as fuzzification which converts crisp input into linguistic variables and formulates fuzzy sets. The second component transforms fuzzy sets into their respective fuzzy soft sets. The third component determines indispensable parameters and performs parameter reduction. The fourth component performs risk analysis by using algorithm. We use exemplary dataset and run all the components of fuzzy soft expert system to compute cancer risk.

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models.

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis.

Development of expert systems for modeling of technological process of pressure casting on the basis of artificial intelligence

In article the main component of expert system of process of casting under pressure which consists of algorithms, united in logical models is considered. The characteristics of system showing data on a condition of an object of management are described. A number of logically interconnected steps allowing to increase quality of the received castings is developed

Construction features of procedural simulator for objective control expert systems management devices of rocket and space technology ground infrastructure objects

Paper refers to the construction of procedural simulators for expert systems management devices of rocket and space technology ground infrastructure objects. A classification of courses, which allows to realize the development of skills with both hardware and software components of a diagnostic expert systems. A method of calculating the integrated assessment of the student mastery of the material.

Neuro-Fuzzy Expert System for evaluating the performance of Distributed Software System Architecture

A Neuro-Fuzzy Performance Evaluation Model (NFPEM) proposed in Akinnuwesi, Uzoka, Olabiyisi, and Omidiora (2012) was reviewed in this work with the view of modifying it and thus making it flexible and scalable. The neuro-fuzzy expert system (NFES) reported in this paper is an enhancement to NFPEM with expert system components. NFES can be used to evaluate the performance of Distributed Software System Architecture (DSSA) with user-centric variables as parameters for performance measurement. The algorithm developed for NFES was implemented using Coldfusion programming language and MySQL relational database management system. The prototype of NFES was simulated using some life data and the performance results obtained point to the DSSA responsiveness to the users' requirements that are defined at the requirements definition phase of the software development process. Thus the performance value is a qualitative value representing DSSA (i.e. system) responsiveness.

Analysis of Expert System in the Field of Machine Design

Intelligent CAD system can be formed if integrating the expert system and mechanical CAD. Components of expert system were analyzed, including integrated databases, knowledge bases, knowledge acquisition, inference engine, explanation mechanism and human-computer interface. The model of design-evaluate-redesign was introduced and discussed. Current situation of research on design expert systems was summarized.

Developing Backward Chaining Algorithm of Inference Engine in Ternary Grid Expert System

The inference engine is one of main components of expert system that influences the performance of expert system. The task of inference engine is to give answers and reasons to users by inference the knowledge of expert system. Since the idea of ternary grid issued in 2004, there is only several developed method, technique or engine working on ternary grid knowledge model. The in 2010 developed inference engine is less efficient because it works based on iterative process. The in 2011 developed inference engine works statically and quite expensive to compute. In order to improve the previous inference methods, a new inference engine has been developed. It works based on backward chaining process in ternary grid expert system. This paper describes the development of inference engine of expert system that can work in ternary grid knowledge model. The strategy to inference knowledge uses backward chaining with recursive process. The design result is implemented in the form of software. The result of experiment shows that the inference process works properly, dynamically and more efficient to compute in comparison to the previous developed methods.

Characteristic factors and components of different modules in expert systems for intelligent tutoring system

Expert systems (ES) have been developed and widely used in the area of tutoring and education. Most of the ES in tutoring systems do not specify its name as ES but these embody the characteristics and features of ES, particularly knowledge representation, reasoning and interface. The different modules of ES in intelligent tutoring system (ES-ITS), i.e., student, pedagogical/tutor and domain (subject) speak of the three components of ES more or less but there is a significant lack of description of various characteristic factors and components that govern the performance of ITS. This work makes a review to identify cognitive, psychological, mental and social components in student model; teaching method and evaluation methodology, problem presentation, evaluation system and feedback strategy in pedagogical/tutor model. Implementation platform and software used by different ES-ITS have been described. Observations in the form of tables and graphs have been made to show the variation of different characteristic factors and components in different modules.

Facet Analysis Techniques for Automatic Classification Systems: A Study for Colon Classification

The method of identification of facets from the title of documents is discussed in this paper. States the technique of selecting respective category name of facets to follow the facet formula in general. Also includes the scope of defining the specific facet formula for any specific subject. Illustrates steps followed in analyzing titles (i.e. in the form of natural language sentence) of documents for finding out noun phrases and picking up isolate numbers, symbols, basic subjects from the frame based knowledge base designed for this purpose. Emphasizes on integration of expert system and natural language processing components to proceed towards a successful automatic classification system.

An expert system using an extended AND–OR graph

The inference engine (IE) is a crucial component of expert systems (ES). As the management environment has become rapidly digitalized with the advent of the Internet, the traditional IE is now facing a severe criticism – i.e., that it cannot effectively provide an agile, knowledge-based decision support suitable for a wide variety of problems. The objective of this paper is to propose a new type of ES called IMIXAO (Integer-Matrix-driven Inference based on an eXtended AND–OR graph), with an integer-matrix-driven IE based on an extended AND–OR graph framework. Traditionally, the AND–OR graph is used for making inferences in the ES area. Theoretical backgrounds of IMIXAO are presented in this paper, focusing on what an extended AND–OR graph is in terms of inference. The experiment conducted in this study, with an illustrative example of dermatological diseases classification, revealed that the proposed IMIXAO can make precise and agile inferences in a complex situation.

A Meta-Analysis of Emerging Infomediary Knowledge Management Systems: An Expert Systems Application in the E-Tailing Environment

Infomediaries provide online retailers or e-tailers with knowledge management systems (KMS), thus serving industry groups rather than the KMS needs of one organization. Of the three main organizational KMS activities, codification is the primary activity of the infomediary. The infomediary also provides expert system capability that uses real time data to learn and to dynamically modify recommendations. Infomediaries are an important new research area. This paper provides an analysis of what is known about infomediary functions: data collection, predicting consumer behavior, and customization of the web site. Then, the infomediary so described is placed within the research domain of KMSs with expert system components. Further research opportunities are identified.