Decision Expert Systems Research Articles

Modeling and computer-aided design of combustion chambers for aircraft engines and gas turbine plants using an expert system

The modeling issues of the working process and the design of combustion chambers for aircraft engines and gas turbines are considered. The developed modeling tool is shown: the “AM” expert decision-making system. A brief description of the expert system and module aimed at modeling and designing of the combustion chamber structures is given. The results of the computer-aided design of combustion chambers for the AL-31F aircraft engine and the AL-31ST gas turbine engine are presented. The results obtained are verified on the basis of the basic design and the values obtained using three-dimensional models of the real structure. A comparison of materials selected by an expert system with those used in a real design is performed. The parametric studies results of the various parameters influence of the combustion chambers working process on their design are also shown.

E-learning based on the adaptive learning model: case study in Serbia

Today’s education faces many challenges related to learning and teaching efficiency, effectiveness, and costs. Contemporary research shows that the learning environment with the ability to adapt to individual needs, requirements, and competencies of students, facilitates the learning process and leads to improved learning outcomes and achievements. Nevertheless, learning management systems (LMS) that are often used in e-learning typically provide a limited level of adaptivity. The goal of this paper is to introduce an adaptive e-learning model which enables personalized learning experience and more intelligent decision making. It consists of the students’ model, the adaptation module, the expert system for data analysis and decision making, the repository of learning objects, and database of educational methods. The designed model provides adaptivity through a learning management system, considering individual characteristics of the student, such as their learning styles and prior knowledge. It is capable to adapt course content, structure, and assessment based on the specific student’s needs and performance. The model is implemented within the widely used open-source LMS, which makes it more usable and easier to deploy. The process of applying the proposed model is illustrated with a higher education case study that shows how the recommended method is applied for a successful transition to an adaptive form of learning. The model has been tested through experiment during which a group of students attended traditional non-adaptive e-learning course, and the other group attended the adaptive e-learning course. The results of data analysis showed that students who learned from an adaptive course achieved better performance in various aspects. The proposed adaptive model can enhance educational processes in terms of improving learning performance, personalized application of teaching/learning methods, as well as continuous improvement cycle.

Early warning expert decision system of water resource management based on Cloud Computing

In order to solve various problems caused by the lack of timely warning in water resources management, based on the current water resources informatization, an early warning expert decision-making system for water resources management based on cloud computing architecture is proposed. The system collects real-time data through the Internet of things interface layer, uses Web GIS to display geographic information and Ajax (asynchronous Javascript + XML) asynchronous interactive processing mode to speed up the response speed. Finally, an early-warning expert decision system for water resource management based on the cloud computing architecture is realized, which can monitor both the command center and the mobile terminal at the same time.

Selection of Diagnosed Quantities of Electromechanical Systems

The article discusses the issues of justification of the selection of diagnostic values for assessing the technical condition of electromechanical systems of equipment. The selection is based on calculation models and models of damage development. The presence of several variants of damage requires constant monitoring of mechanical and electrical values analyzed by the decision-making block, taking into account possible scenarios. Monitoring of the actual state of the electromechanical system is possible when using a set of diagnostic values: technological process, vibration, temperature, current and load characteristics. The analysis of current values requires the use of continuous monitoring systems, diagnostic models that take into account the actual operating mode, and expert decision-making systems focused on fixing slow and fast changes. It is advisable to restore the working condition of the equipment on the basis of a system of scheduled preventive repairs, taking into account the actual condition. A prerequisite is the use of visual inspection, non-destructive testing methods (if necessary), and lubrication analysis methods.

Retraction: Early warning expert decision system of water resource management based on Cloud Computing (J. Phys.: Conf. Ser. 1648 042096)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 16 September 2022

Development of a Prototype of an Expert System for Decision Making Support in Optimizing Measures Aimed to Protect Water Objects from Diffuse Pollution: Case Study of the Volga Basin

A prototype of an expert system for decision-making support in optimizing measures aimed to protect pilot water objects from diffuse pollution in the Volga basin was developed based on special expedition studies carried out in 2018–2019, generalized results of earlier long-term studies, improved methodological approaches, and specially developed GIS. The prototype consists of fine interrelated blocks, which can be used to evaluate the anthropogenic load from different types of pollution sources (point and diffuse) and the parameters that characterize this pollution, and describe the methods and results of diffuse runoff calculation as well as possible water protection measures, and their efficiency. The prototype of the expert system allows the users (decision makers) to evaluate the extent of diffuse pollution and to choose the priority measures to reduce its effect on pilot water objects.

Develop of Expert System for Early Diagnosis and Prevention of Mastitis

Expert systems (ES) are one of the prominent research domains of artificial intelligence (AI). They are applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise. We present design and development of expert system for data collection, analysis and decision making for early mastitis detection. It focuses on both milk quality and animals health.

Application of fuzzy expert decision-making system for rock slope block-toppling modeling and assessment: a case study

The strategy applied in this study is fuzzy logic based decision-making system to achieve a rapid way to assess block-toppling failure instability in discontinuous rock slopes as justified by kinematic analysis that are applied to real cases. Referring to fuzzy logic based decision-making; the best option was selected from multiple fuzzy variables through performing a comparison and by obtaining the fastest solution for approximation. The expert system offers a capable fuzzy application for engineering judgment to be utilised in geotechnical decision making for engineering design. Kinematic analysis is currently used for the stability assessment of all types of toppling failures which provides the ‘occurrence possibility’ and is not capable of describing the stability conditions (degree of occurrences) in discontinuous rock slopes. Application of fuzzy logic complements kinematic analyses through giving a more accurate stability assessment. This study offers a complementary method for block-toppling failure instability assessment in discontinuous rock slopes based on artificial intelligence and a fuzzy expert decision-making system.

Machine learning methods in organ transplantation.

Machine learning techniques play an important role in organ transplantation. Analysing the main tasks for which they are being applied, together with the advantages and disadvantages of their use, can be of crucial interest for clinical practitioners. In the last 10 years, there has been an explosion of interest in the application of machine-learning techniques to organ transplantation. Several approaches have been proposed in the literature aiming to find universal models by considering multicenter cohorts or from different countries. Moreover, recently, deep learning has also been applied demonstrating a notable ability when dealing with a vast amount of information. Organ transplantation can benefit from machine learning in such a way to improve the current procedures for donor--recipient matching or to improve standard scores. However, a correct preprocessing is needed to provide consistent and high quality databases for machine-learning algorithms, aiming to robust and fair approaches to support expert decision-making systems.

ZSLF: A New Soft Likelihood Function Based on Z-Numbers and Its Application in Expert Decision System

Due to the complexity of the real world, effective consideration of the ambiguity and reliability of information is a challenge that must be addressed by the correct decision of the expert system. Z-number provides us with a good idea because it describes the probability of the random variable and the possibility measure. Recently, Yager presented a soft likelihood function that effectively combines probabilistic evidence to deal with the conflict information. This article generalizes Yager's soft likelihood function based on Z-numbers and proposes a Z-numbers soft likelihood function (ZSLF) decision model. The application examples show the rationality and effectiveness of the method. The comparison and discussion further show the advantages of the ZSLF decision model.

Estimation of linear motion in dense crowd videos using Langevin model

Expert and intelligent systems are highly popular for designing cross-domain autonomous systems. Computer vision aided by expert decision-making systems are widely used to automate tasks that are normally carried out manually. For example, in the traditional way of traffic monitoring, signals are either controlled through predefined set-ups or with the help of visual observations. Even though some of the modern cities employ sensor-based surveillance at large, full automation is still far from the desirable accuracy. This is more challenging for monitoring high-density crowds that often cause unwanted situations due to sudden changes in dynamics. It has been shown in this paper that the movement of a dense crowd can be approximated using well-known physics-based models. Such a modeling can help to understand the overall crowd behavior. In accomplishing this, we have introduced a computer vision guided expert system with the help of a Langevin equation-based force model to represent the linear flow of the crowd, particularly in situations when the density is high. One of the primary contributions of our proposed model is its computational efficiency, particularly when a timely decision can help to avoid unwanted situations. Our proposed three-term force model is capable of predicting the positions of the group of key-points in a video frame leading to a significant computational gain. We have carried out several experiments on publicly available videos as well as our own videos to validate the claims in terms of accuracy as well as computational gain. It has been observed that the proposed physics-based model outperforms the existing systems with a 4−6% improvement in the segmentation accuracy. Moreover, we have achieved multi-fold computational gain. We believe the proposed work, when supported by appropriate post-processing, can be used to develop crowd monitoring applications.

A hybrid approach to prioritize risk mitigation strategies for biomass polygeneration systems

Biomass polygeneration system is one of the most attractive biomass technologies due to its technicality, feasibility and high associated investment returns. The synthesis, design and economic aspects of constructing a processing system using this technology are well-developed and have recently reached the stage of industrial implementation. Nonetheless, the early stage of technology development focuses on process and product safety and tends to ignore other risk aspects that are closely associated with the biomass value chain. Due to the complex nature of the biomass value chain, conventional risk mitigation strategies are ineffective in mitigating risks at the management level. More recent approaches, particularly stochastic programming methods, have yielded robust results in addressing technological risks and design uncertainties. However, such approaches are still unable to effectively consider non-quantitative risks such as business risks and regulatory risks. Hence, this study proposes a combined method of an analytical model and stochastic programming approach to prioritize risks and risk mitigation strategies for decision-making purposes. This work presents a novel multiple-criteria decision-making expert system based on fuzzy set theory, which is the Decision and Evaluation-based Fuzzy Analytic Network Process (DEFANP) method. The novel method functions to prioritize risk mitigation strategies within a network relationship of project goals, key components of the biomass industry and industrial stakeholders. As the stochastic risk mitigation counterpart, the fluctuations and uncertainties in operations, transportation, market supply-demand and price are modeled using the Monte Carlo simulation method. From this, risks of implementing biomass polygeneration systems can be mitigated by selecting a strategy that yields the highest analytical indicator while reconciling with the corresponding probabilities of achieving management goals. A palm biomass polygeneration system in Malaysia is presented as case study where the key implementation risks are regulatory risks, financing risks, technology risks, supply chain and feedstock risks, business risks, social and environmental risks.

ІНФОРМАЦІЙНО-МАТЕМАТИЧНЕ ЗАБЕЗПЕЧЕННЯ ТЕХНОЛОГІЇ ДЕСУЛЬФУРАЦІЇ ЧАВУНУ

The concept of the expert decision-making system for technological process management, created earlier at the Iron and Steel Institute of the National Academy of Sciences of Ukraine, provides for the choice of a rational technology for out-of-furnace desulfurization of cast iron. The purpose of this work is the further development of information and mathematical support of the expert system module for out-of-furnace processing of cast iron by co-injection of magnesium and lime in the framework of the target project to create end-to-end technology for high quality metal products. An integrated database of cast iron desulfurization process of a number of metallurgical enterprises of the People's Republic of China and the domestic DMK plant is presented. Based on the analysis of technological data, empirical equations were obtained to determine the final sulfur content in the cast iron melt, as well as the expression for estimating the specific consumption of metallic magnesium in the reagent. The developed models for calculating the specific consumption of reagents are the basis of the algorithm for calculating the indicators of the process of desulfurization of cast iron with co-injection of magnesium and lime. The input parameters are: temperature of cast iron, mass of cast iron, chemical composition of cast iron and slag, sulfur content initial and final, the proportion of magnesium in the reagent. Replenishment of the database with information on the technology of desulfurization of cast iron with a mixture of magnesium and lime allowed to clarify the existing and obtain descriptive models for these reagents. The developed information and mathematical support of the subsystem of out-of-furnace processing of pig-iron takes into account material and energy costs for redistribution, providing resource savings and the required quality of metal products.

Рациональное использование водных ресурсов с автоматизированным расчетом полива посевов на орошаемом поле

Crop production is accompanied by a set of tasks, each of which for different soil and climatic conditions has its own characteristics and standards. Irrigation is one of the types of water reclamation in areas with insufficient and unstable natural moisture, aimed at preventing soil and partially atmospheric droughts. Determination of crops moisture supply and irrigation norms is the most important task when growing crops in arid regions. The objective of our work is to develop an automated information technology for decision-making during irrigation. The main indicators of moisture supply of the irrigated field is the reference, potential and actual evapotranspirations, crop water stress index. Indicators of moisture supply are determined according to the monitoring of agro/meteorological, physiological parameters of crops. For monitoring tasks in the Agro/physical Institute (St. Petersburg), an automated mobile field agro/meteorological complex (AMFAC) was created. The software of the complex allows real-time saving of synchronously measured data time series, which are used for automated calculation of reference, potential and actual evapotranspirations, crop water stress index and irrigation norms. As a result of the field experiments, methods for assessing the evapotranspiration and crop water stress index were tested. Automated information technology, including monitoring, the creation of a database, and computational algorithms for models of energy exchange in the «soil – plant – atmospheric surface layer» system can serve as the basis for an expert decision-making system for irrigation.

A multi-criteria expert decision system for investment decisions: The case of commercial real estate investments in China

Consistent with the rapid growth of the Chinese economy and its commercial real estate (CRE) market, evaluating CRE investments in China is becoming an important application area where a multi-criteria expert decision system can make a significant contribution. We used a multi-criteria expert decision system to evaluate ten actual CRE investments in China. Based on thirty-one industry experts, we identified key decision factors to consider when evaluating different CRE investment projects and determined their importance weights. We then evaluated whether or not the ten CRE investment projects were considered to be successful, taking into account the unique characteristics of each CRE investment project. We finally compared the evaluations for the ten cases with their actual performance using four different performance indicators. The results showed that the evaluations had at least 87.5% accuracy across four different performance indicators. As probably one of the first systematic and in-depth scholarly studies to evaluate CRE investment projects in China, this study contributes to a better understanding of such evaluations and helps CRE investors evaluate CRE investment projects in a more informed fashion.

Globally-biased BIRECT algorithm with local accelerators for expensive global optimization

In this paper, black-box global optimization problem with expensive function evaluations is considered. This problem is challenging for numerical methods due to the practical limits on computational budget often required by intelligent systems. For its efficient solution, a new DIRECT-type hybrid technique is proposed. The new algorithm incorporates a novel sampling on diagonals and bisection strategy (instead of a trisection which is commonly used in the existing DIRECT-type algorithms), embedded into the globally-biased framework, and enriched with three different local minimization strategies. The numerical results on a test set of almost 900 problems from the literature and on a real-life application regarding nonlinear regression show that the new approach effectively addresses well-known DIRECT weaknesses, has beneficial effects on the overall performance, and on average, gives significantly better results compared to several DIRECT-type methods widely used in decision-making expert systems.

Primjena ekspertnih sustava odlučivanja u prijevozu

Primjena ekspertnih sustava odlučivanja u prijevozu

Pattern recognition in expert decision-making systems

The paper presents a review of pattern recognition methods in various expert decision-making systems. In particular, the methods of visual images recognition are studied. The relevance of the proposed study is that in almost all spheres of life and production computer processes huge amounts of information at high speed, using certain algorithms, but people should anyway participate in decision-making. Pattern recognition in various processes gives the possibility to achieve maximum production results. Implementation of neural networks into production processes provides for maximum production optimization. The main task for artificial intelligence is to create certain information systems with an effective level of solutions for various non-standard tasks. Considering that, neural networks make it possible to recognize images in different decision-making systems. The subject of research is a review of pattern recognition methods in expert decision-making systems. The ability of pattern recognition in the considered systems is shown. It is demonstrated that the variety and complexity of recognition tasks do not provide implementation of one universal approach to the solution. The paper proposes modified classification of pattern recognition methods and implementation of neural networks into production process of Kamaz Public Company.

Physician Review of a Celiac Disease Risk Estimation and Decision-Making Expert System

Objective: Celiac disease is a genetic disease affecting people of all ages, resulting in small intestine enteropathy. It is considered to be a clinical chameleon. Average prevalence of celiac disease is 1 out of 100 people with data indicating the risk may be as high as 22% for those with first-degree relatives with the disease. Eighty-three percent of people with celiac disease may be undiagnosed. Average duration to diagnosis is 10 years. Data indicate that there is a lack of consensus regarding diagnostics and symptomatology.Method: A clinical decision support system (CDSS) was developed using Exsys Corvid for expert analysis (CD-CDSS). The CD-CDSS was divided into symptoms and manifestations with 80 points of navigation, and a serology section, and was validated by 13 experts in the field of celiac disease using a 10-statement 5-point Likert scale.Results: This scale was analyzed using Cronbach’s alpha reliability coefficient, which was calculated using SPSS and revealed good internal consistency and reliability with a result of 0.813. One hundred percent of experts agreed that the CD-CDSS is capable of guiding a health care professional through the diagnostic process, contains an accurate list of symptoms based on the clinical literature, and can foster improved awareness and education about celiac disease and that there is a need for this system.Conclusions: A celiac disease risk estimation and decision-making expert system was successfully developed and evaluated by medical professionals, with 100% agreeing that this CD-CDSS is medically accurate and can guide health care professionals through the diagnostic process.

Application of Intelligent Temperature Control Technology in Strawberry Greenhouse

China's greenhouse crop cultivation, flower cultivation and so on are still in a slightly backward stage. As a large agricultural country, it is very important to produce a thermostatic greenhouse with low price, strong environmental universality and stable performance. The development of artificial intelligence technology promotes the development of intelligent agriculture [1]. The agricultural expert decision-making system based on artificial intelligence technology improves the accuracy of agricultural knowledge acquisition and decision-making expression, and promotes the development of agriculture towards precision [2]. Therefore, it is very important to realize intelligent control of ambient temperature in order to meet the demand of growing environment in strawberry greenhouses. Based on the problem of temperature control, a fuzzy PID control method is proposed to realize the self-tuning of PID parameters and meet the requirements of PID parameters regulation in the temperature control process. In addition, compared with the conventional PID algorithm, the fuzzy PID algorithm has the characteristics of short rise time, fast response speed and high control accuracy. The fuzzy PID algorithm has good control performance on the temperature control system.