Expert System Technology Research Articles

Optimizing water resources for sustainable desalination: The integration of expert systems and solar energy in experimental applications

The integration of renewable energy sources with multi-energy systems present challenges and opportunities to enhance sustainability. Among these, solar stills have emerged as a solution for water desalination. With the advent of expert system technologies, avenues are opened for improving the operational efficiency of solar distillers. This paper presents an innovative approach utilizing correlation analysis, ReliefF for feature selection, and a k-Nearest Neighbor (kNN) algorithm for forecasting the cumulative distillate output of a double slope solar still. The analysis is based on a 6-cases-based dataset, which includes variations in distillate output relative to different operational-environmental conditions. Key features that significantly impact overall performance were identified to manage the solar distiller productivity. The findings reveal that the maximum distillate output was 1610 ML/m2.day due to incorporating reflective materials and phase change materials (PCM) in enhancing distillation rates. The kNN model was evaluated based on its R2, RMSE, and CVRMSE, with the best models achieving scores of 0.995, 0.0033, and 0.1666, respectively. These metrics underscore the effectiveness of the proposed machine learning approach in predicting distillate output, thereby enabling informed management of solar distillation processes. Combining renewable energy technologies and computational intelligence holds significant promise for sustainable environmental management, as the study presented.

Enhancing Supply Chain Safety and Security: A Novel AI-Assisted Supplier Selection Method

The "Make or Buy” decision and the supplier selection are critical steps for the efficient operation of companies' supply chains. Safety and security are paramount considerations, especially in industries like logistics, where supply chains are vulnerable to external threats and disruptions. In this scientific article, we present a novel Artificial Intelligence (AI)-assisted supplier selection method that significantly enhances the safety and security of suppliers. During our research project, we have created an expert system and a corresponding knowledge base with the relevant rules to support supply chain decision-makers in selecting logistics service providers for warehousing services. The foundation of the AI-assisted supplier selection method is advanced data analytics and the application of expert systems, enabling companies to evaluate potential suppliers in detail from a safety and security perspective. The applied expert systems can identify potential risks and make predictions about supplier performance in the future. In the turbulent environment of the global supply chain, selecting long-term partners like warehousing services providers is critical for the success of the organization. A wrong supplier selection can hardly be reversed in warehousing services, as the cost of change is usually high. The article demonstrates the practical application of the expert system-assisted supplier selection method in a real-world supply chain environment and thoroughly analyzes the achieved results and advantages. The results show that the expert system-assisted method not only increases supplier safety and security but also contributes to improving the efficiency and sustainability of the supply chain. This article provides valuable guidance and solutions for companies looking to enhance their supplier selection using expert system technologies, thereby increasing the safety and security of their supply chains.

Penegakan Diagnosa Keperawatan pada Pasien Gastroesofagus Reflux Disease dengan Aplikasi Sistem Pakar Forward Chaining

This study aimed to see the use of a computer-based expert system with the forward channeling method for detecting Gastroesophageal Reflux Disease and its application in nursing. The research method used in writing this literature review is based on 25 pieces of literature from 2018-2022. This expert system is run with PHP and MYSQL programs. The results of this study are that the Forward Chaining Expert System program will detect gastroesophageal reflux disease based on the symptoms inputted into the system and display an intervention plan that can be carried out like an expert. Still, this system cannot yet display 100% appropriate disease diagnosis because it requires a series of other tests that have not been prepared. In conclusion, with this expert system technology, health workers and users are increasingly assisted and shorten the time needed for disease diagnosis and treatment.
 
 Keywords: Expert System, Forward Chaining, Gastroesophageal Reflux Disease

Automated method for selecting optimal digital pump operating strategy

Commercially available hydraulic pumps exhibit lower performance at a wide range of operating conditions, resulting in a significant decrease in productivity and efficiencies as low as 30%. A new class of digital pump technology has been developed to overcome these limitations. This work builds upon a previously developed high-efficiency digital pump that utilizes high-speed on/off valves. There are four distinct strategies in which the digital pump technology can operate, and each of these strategies offers unique advantages that make it suitable for specific operating conditions. In this work, we introduce a novel approach that utilizes expert systems technology in combination with several automated machine-learning methodologies to determine the most efficient operating strategy. Different supervised machine-learning algorithms are investigated to predict the overall efficiency of each operating strategy. The best machine-learning model is then employed for selecting the most efficient operating strategy given any operating condition. Given the findings, the efficiencies predicted by the machine-learning model align well with the efficiencies measured through experiments. This innovative approach has resulted in substantial energy savings compared to the currently available pumps in the market.

Forecasting methods of intelligent systems technical condition analysis

Modern information processing and control systems reached the level that makes it possible to use detailed information on device and human nervous system functioning. Using a cognitive psychological approach in intelligent systems development, allowed them to imitate human nervous system functions. The development of increasingly powerful intelligent information and control systems with learning and self-learning, including information and measuring systems, is expected as a result.
 They will have enhanced cognitive capabilities due to the stimulation of cognitive functions and processes responsible for perception, learning, thinking, and consciousness in the human nervous system.
 Development of this field allows to develop intelligent systems with thinking and behaviour analysis elements. Adding some creative possibilities, for example, related to automatic hypotheses and models creation and self-learning for new task solving, allows to improve the efficiency of intelligent systems.
 Due to this new approaches to the artificial brain and the artificial nervous system of robots development, which relate not only to artificial intelligence but also to its development in the form of an artificial mind.
 Thus, the main scientific and development task for models, methods, methodics, and algorithms for intelligent systems technical condition forecasting based on soft computing is important and relevant for science and practice.
 Intelligent technologies allowing to develop useful intelligent systems are continuously being improved. Currently, quite powerful tools for implementing the technology of expert systems, fuzzy logic, neural network systems, and multi-agent systems technology are used. They are rapidly improved by adding software packages and hardware tools.
 New technologies are being developed in the so-called neuromorphic systems field that models some brain structures and in the parallel computing and quantum computers field. These technologies aim to raise significantly the intelligence level of systems in the future.

You’re Not the Boss of me, Algorithm: Increased User Control and Positive Implicit Attitudes Are Related to Greater Adherence to an Algorithmic Aid

Abstract This study examined whether participants’ adherence to an algorithmic aid was related to the degree of control they were provided at decision point and their attitudes toward new technologies and algorithms. It also tested the influence of control on participants’ subjective reports of task demands whilst using the aid. A total of 159 participants completed an online experiment centred on a simulated forecasting task, which required participants to predict the performance of school students on a standardized mathematics test. For each student, participants also received an algorithm-generated forecast of their score. Participants were randomly assigned to either the ‘full control’ (adjust forecast as much as they wish), ‘moderate control’ (adjust forecast by 30%) or ‘restricted control’ (adjust forecast by 2%) group. Participants then completed an assessment of subjective task load, a measure of their explicit attitudes toward new technologies, demographic and experience items (age, gender and computer literacy) and a novel version of the Go/No-Go Association Task, which tested their implicit attitudes toward algorithms. The results revealed that participants who were provided with more control over the final forecast tended to deviate from it more greatly and reported lower levels of frustration. Furthermore, participants showing more positive implicit attitudes toward algorithms were found to deviate less from the algorithm’s forecasts, irrespective of the degree of control they were given. The findings allude to the importance of users’ control and preexisting attitudes in their acceptance of, and frustration in using a novel algorithmic aid, which may ultimately contribute to their intention to use them in the workplace. These findings can guide system developers and support workplaces implementing expert system technology.

Expert systems! Who knows?

A number of aspects of knowledge representation in expert systems are discussed. The aim is mainly to introduce to them, and as regards expert system technology the discussion is quite introductory, the perspective being more that of simple practice in the fieldthan state-of-the-art applications. The main subject is how knowledge is embodied in expert systems, in particular with respect to explicitness and articulation. It is suggested that expert systems may embody knowledge in ways more similar to human knowledgerepresentation than do conventional computer systems. Important reservations concerning this claim are also made.

Short-Term Forecasting of Power Consumption of Power Supply Companies Based on the Integration of Technologies of Analytical, Simulation and Expert Systems

Purpose of research is to improve software support and identify regularities in the processes of short-term forecasting of power consumption of power supply companies based on complementary integration of data mining models, system dynamics and expert systems.Methods. The principles of constructing predictive models of power consumption are given. A system analysis has been carried out and an ontological model of the subject area has been built, taking into account the technological and market environment. The classification of forecasting methods has been considered. The features of the information base for short-term forecasting, including data on actual power consumption and weather data, have been described. The requirements for software for making forecasts have been formulated. A block diagram of the system for forecasting power consumption of the market for the day ahead is built based on the complementary integration of data analysis and modeling software.Results. Scenarios for data processing in Loginom have been developed using the Arimax and Neural Network (Regression) processors to build forecasts based on actual power consumption and taking into account meteorological factors. A system dynamics simulation model that allows exploring the influence of meteorological factors (temperature, pressure, precipitation) on power consumption has been developed in Anylogic. Using Wi!Mi mivar constructor of expert systems, the task has been parametrized; indicators, relationships, rules have been set; a logical conclusion of the solution has been obtained.Conclusion. A block diagram of a system for forecasting the market's power consumption for the day ahead has been built. It is based on the analysis of retrospective information on actual power consumption and meteorological factors using data mining methods, system dynamics and expert systems applying Russian Loginom, Anylogic and Wi!Mi software tools.

Application of Certainty Factor Method in Intelligent System for Diagnosis of Periodontal Disease in Cigarette AddictsApplication of Certainty Factor Method in Intelligent System for Diagnosis of Periodontal Disease in Cigarette Addicts

This study discusses the design of an Android-based innovative application that is useful in the early diagnosis of periodontal disease in cigarette addicts. The problem which is the main topic of discussion in this study is the health issue of organs that are often ignored by humans, namely the teeth and mouth. Then one of the types of dental and oral diseases that many people often complain about is the periodontal disease which also gets less attention in Indonesian society. And one of the causes of this periodontal disease is caused by smoking habits. So to facilitate the identification in knowing the periodontal symptoms caused by the cigarette, a system that can identify the early symptoms of periodontal disease is needed. The technology proposed to build the system applies expert system technology with Certainty Factor. In building an android-based innovative application to diagnose periodontal disease, there are cigarette addicts in this study with the Research & Development research method to produce an output that can be right on target by the expected target. In addition, interviews and direct observation techniques were also carried out with experts or experts in the field of dental and oral diseases to collect the required data on the needs of the system to be built.

Metode Forward Chaining Untuk Mendiagnosa Kerusakan Mesin Mobil Toyota Innova Berbasis Web

The growing development of the automotive industry in Indonesia has an impact on increased car sales. More and more people are buying cars because they have become daily transportation. Damage experienced in the car is handled by experts or expert technicians. Diagnosis of damage is carried out by a technician based on signs of initial damage to the car. This encourages experts to develop new computer systems to support human work. The development of computers today has undergone many very rapid changes, along with the increasing and complex human needs. then we need a method or system that can diagnose damage to the car. One of them is by utilizing artificial intelligence technology. One sub-field of artificial intelligence is an expert system. By utilizing expert system technology can be produced a system to diagnose car engine damage. This expert system discusses the diagnosis of web-based Toyota Innova damage using the forward chaining method. With the aim of helping the community to diagnose the damage to the Toyota Innova car engine.

Sistem Pakar Diagnosa Penyakit ISPA dengan Metode Forward Chaining

The development of computer technology today continues to experience many changes every year, which are often developed by artificial intelligence, such as expert system technology. An expert system is a computer-based application that can match or imitate the ability of an expert used to solve problems that cannot be solved by ordinary people. His knowledge is taken from books, experience and knowledge. Children often experience ISPA disease caused by viruses and bacteria because children's immune systems are still susceptible to being different from adults. This disease usually begins with a hot body temperature accompanied by symptoms such as sore throat or painful swallowing, runny nose, dry cough and others. So that many parents do not know the symptoms of ISPA disease, as for some ways to prevent ISPA disease are diligently washing hands, increasing consumption of foods containing vitamins, exercise. To make it easier for parents to detect ISPA disease, the authors made this study using the forward chaining method, using this method the resulting system is a system that provides a choice of several symptoms then based on the selected symptoms conclusions will be drawn. This ARI disease expert system uses blackbox testing because blackbox testing is a software testing technique that focuses on the functional specifications of the software or system. Blackbox testing is done on each submenu view, input, edit, print, delete data. So that it will produce an expert system to diagnose ISPA in children online

ТЕХНОЛОГИИ КОНСТРУИРОВАНИЯ ЭКСПЕРТНЫХ СИСТЕМ ДЛЯ ЭЛЕКТРОЭНЕРГЕТИКИ

The technologies of construction diagnostic expert systems is considered. The strategy of the knowledge base structuring by diagnostic expert system is described

The method of transformation of the model of the organizational and technical system into the model of the information system

A variety of systems and decision-making processes have their own characteristics. First of all, these problems are difficult to describe algorithmically. Decisions are made according to certain scenarios, for the description of which it is advisable to use knowledge bases and expert system technologies. When automating the activities of a decision maker, software intelligent agents can be used. Therefore, for such processes, the information system should include a decision support system that will help the decision maker, based on the available information, correctly identify the problem and choose the optimal solution. It should be noted that all algorithms and behavior scenarios are subject to complete formalization. In some cases, the direct participation of the decision maker is required. Organizational and technical systems are characterized by decision-making processes that involve working with knowledge, are formalized by scenarios, and in some cases involve the coordination of decisions. The effect of informatization will be much higher if the task of automation is solved together with the task of improving business processes. At a certain stage of the analysis of the terms of reference and business processes, it is necessary to develop a conceptual model: to identify and describe the main objects and the relationships between them, in order to formalize the subject area of the organizational and technical system, which, in the future, must be transformed into a conceptual model of the information system. The article discusses a method for transforming a model of an organizational and technical system into an information system model, which is formed as a set of diagrams of functional and object-oriented approaches based on the use of conceptual domain models. This method allows building a one-to-one correspondence between the conceptual model of the subject area of the organizational and technical system and the model of the information system.

Improved Random Forest Fault Diagnosis Model Based on Fault Ratio

Abstract With the rapid development of information technology, the informatization, integration and complexity of more and more large equipment are increasing day by day, so it is very important to carry out fault diagnosis for such complex equipment. In the traditional way, expert system technology is usually used for fault diagnosis of complex equipment. However, with the increasing of equipment data information, traditional methods cannot solve the fault diagnosis requirements in the case of a large amount of data. Therefore, data-driven fault diagnosis method can solve this problem, The carrier of data-driven fault diagnosis is a large amount of engineering data, and its focus is to explore new methods of fault diagnosis from a large amount of historical data. In this paper, the classical random forest algorithm is selected as the basic model, and aiming at the imbalance of complex equipment data, the improved random forest voting mechanism based on the fault ratio is proposed to optimize the model, which makes the final model diagnosis accuracy more than 95%, and has good application value.

The Optimization Method of Mechanical Fault Diagnosis Based on Artificial Intelligence Technology

In order to overcome the problems existing in the traditional mechanical fault diagnosis, and to improve the accuracy of mechanical fault diagnosis, an optimization method of mechanical fault diagnosis based on artificial intelligence technology is proposed in this paper. The optimization method combines a variety of current popular high and new technologies, such as artificial intelligence technology, expert system and artificial neural network technology. at the same time, the optimization method also breaks the traditional mechanical fault diagnosis mode based on fuzzy set theory. Based on this, the optimization method also makes an in-depth analysis and research on artificial intelligence technology, which promotes the application and development of artificial intelligence technology, and expands the application scope and application field of artificial intelligence technology. The research results show that the optimization method can scientifically overcome the problems existing in the traditional mechanical fault diagnosis, and can improve the accuracy and scientificalness in the process of mechanical fault diagnosis, so it has strong feasibility.

DEVELOPMENT OF ONTOLOGICAL DECISION MAKING SYSTEM OF THE NEGOTIATION PROCESS ON COOPERATION PRODUCTION IN AIRCRAFT

Subject matter: means of information support for the negotiation process on the organization of cooperative production of aircraft. Goal: an outline of the process of developing effective means of information support for negotiations on cooperative production, which will provide financial savings, firstly, by reducing the negotiation time, and secondly, is reducing the likelihood of making irrational decisions that can lead to additional costs during project implementation. Tasks: Increase the efficiency of the negotiation process on the organization of cooperative production by creating a problem-oriented methodological basis, and on this basis - software tools for the development, deployment and operation of the appropriate decision support system. Methods: system analysis and set theory, for the formal presentation of relevant information about the subject area "Negotiations on cooperative aircraft production"; ontological engineering, in particular the IDEF5 standard, as well as elements of expert systems technology, for building an ontological decision support system. Results: an aggregate of methodological tools for the synthesis of ontological decision support systems was developed during negotiations on the organization of cooperative production of aircraft, a scenario example of organizing an ontological dialogue in the environment of an ontological decision support system for defining an aircraft model as an object of cooperative production was given. Conclusions: the need to supplement the mathematical support of traditional negotiation support systems with special methodological means is shown to increase the efficiency of information support for the negotiation process on organizing cooperative production in aircraft construction; it is substantiated that for the program implementation of the system of information support for negotiations on the organization, it is advisable to use the ontological approach; the architecture of the intellectual core of the ontological decision support system for organizing the negotiation process for organizing the cooperative production of aircraft in the form of a hierarchy of frames, with the organization of knowledge-based inference by means of attached procedures is proposed; a scenario example of the formation of a decision on the choice of an aircraft model for the organization of cooperative production in the environment of an ontological decision support system is given.

Sistem Pakar Troubleshooting Kerusakan Hardware Laptop Dengan Metode Backward Chaining Berbasis Android

Laptop is a very helpful device for human work. Almost all fields use laptops to complete human work. Expert system technology that serves as a substitute for someone who is an expert in their field. Expert systems arise because of problems in a specific specific field, where users want a solution to the problem solved by approaching expert ways in solving problems. There are several methods in expert systems. This study will discuss the Expert System for Troubleshooting Hardware Damage on Laptops, and the inference method used is Backward chaining. Tracking is based on data or facts and then leads to conclusions in the form of conclusions about symptoms of laptop damage or problems that attack the laptop. The system that has been built is tested by comparing the suitability of the system output with the results of expert diagnoses. The test results from 15 case data obtained an accuracy rate of 86.6%. With these accuracy results, it shows that the system is able to help the role of experts

Research on Intelligent Flight Test Monitoring Technology Based on Expert System

The current manual-based traditional flight test monitoring technology can no longer fulfill the requirements of high integration, system complexity and technological advancement of new aircraft. In addition, the existing flight monitoring systems are mostly single-point, single-system applications, including ground-assisted systems such as meteorology, radar, and response system. It is easy to cause problems such as data islands, which is not conducive to data sharing and comprehensive evaluation and analysis. This paper proposes a new method for analyzing and mining existing test data based on expert system technology to build a flight test expert system library. A new real-time monitoring system is designed that can meet the real-time processing and integration of multi-source data, comprehensive evaluation and analysis, intelligent warning and assistant decision-making. This system expands the function and intelligence of the existing system, and further improves the quality, safety and efficiency of the flight test monitoring system.

Don’t touch my stuff: historicising resistance to AI and algorithmic computer technologies in medicine

ABSTRACT This paper historicises the criticisms and backlash from within medicine against new computer technologies in the clinic. Physicians' reactions to proposals for the implementation of algorithmic technologies in the clinic ranged from enthusiastic to cautionary to critical from as early as the 1960s. Clinicians were suspicious of these technologies as they threatened their professional expertise. I argue that these discontent reactions from doctors demonstrate an implicit struggle for authority over clinical spaces and with regards to medicine's place within society more generally. Drawing on Foucault's concept of discursive rules and their function within a closed community, I recover the forgotten debate to include or reject AI and its predecessor technologies of expert systems and neural networks. This paper explains how and why justifications for and against the applicability of AI to the clinic are underpinned by questions of medical authority. I conclude with an inquiry into the transformative possibilities of partisanship.

Research on Intelligent Prescription Review System Based on Medical Big Data

According to statistics, the incidence of adverse drug reactions in my country is 10%-30%, and about 190,000 people die from serious adverse drug reactions each year. Therefore, relying on the pharmaceutical knowledge base to provide clinicians with real-time rationalized medication diagnosis and treatment opinions, and to prevent the occurrence of contraindications, overdose and other wrong medications, is an important topic worthy of medical information research. Therefore, this article introduces the expert system and ontology technology into the application of smart medical services, and builds a prescription intelligent review system based on the pharmacy knowledge base, and through the construction of prescription ontology, pharmacy knowledge base modeling, and review process reorganization During the process, AI intelligent review of prescriptions issued by doctors is carried out in real time.