Search

The article systematizes the factors influencing reliability, identifies the failure rate at the operating stage for research, and proposes a fuzzy model for correcting the failure rate, taking into account the influence of real factors during operation. The results were tested on a Siemens-SGT-800 gas turbine. [ABSTRACT FROM AUTHOR]

The inspection of building components plays a crucial role in reducing maintenance costs throughout the building's life cycle. Many buildings suffer from defects in the building elements, such as surface problems, cracks, and moisture problems. Building Information Modeling (BIM) has become a widely used digital tool for the construction industry, allowing for the virtual visualization, design, and simulation of structures. At the same time, progress in neural networks, a branch of artificial intelligence, presents encouraging potential for classification and evaluation. This article explores the fusion of BIM and neural networks to improve and simplify the building inspection procedure. Neural networks are implemented with BIM to conduct real-time analysis and predictions as part of an inspection framework. A data set consisting of 500 wall samples with defects from different buildings was collected through fieldwork. These data were then classified into five categories, ranging from D1 (No damage) to D5 (Collapse), based on expert opinions. A neural network was used to create a model that can show the severity of degradation of building elements. The BIM platform is utilized in the implementation of the proposed model to facilitate the exchange of information and enhance documentation during inspection. The visualization provided by BIM allows the facility management team and building owners to assess building elements through causality analysis using distinct color codes. A case study of an office building is featured in the paper to showcase the application of the proposed model for asset management. The color-coded system used in this study denotes low-performance conditions as red, medium performance conditions as yellow, and high-performance conditions as green. This article outlines the procedures for categorizing and ranking defects in building elements, specifically focusing on walls. Practical Applications: This study explores innovative methods for assessing the condition of buildings, aiming to improve accuracy, efficiency, and predictive capabilities. Traditional building inspections are often subjective, time-consuming, and labor-intensive, making it challenging to generate consistent and comprehensive evaluations. Our research addresses these limitations by integrating BIM and neural networks. BIM provides detailed digital models of buildings, which include comprehensive information about each component. This technology enables precise and consistent assessments, reducing the subjectivity inherent in manual inspections. Neural networks, a type of artificial intelligence, can analyze complex data patterns and detect potential issues early, allowing for proactive maintenance. By combining these two technologies, our approach offers a more efficient and objective method for building condition assessment. This integration not only saves time and resources but also enhances the ability to predict and prevent future issues, ultimately extending the lifespan of building components and improving overall building performance. These advancements are particularly relevant for property managers, maintenance teams, and construction professionals seeking to optimize building maintenance and management practices. The findings of this study can significantly benefit the construction and facilities management industries by providing a more reliable and advanced method for maintaining building health and safety, thereby ensuring sustainable and cost-effective building management. [ABSTRACT FROM AUTHOR]

Abstract In this study, a fuzzy model is presented for predicting the possibility of degradation due to salt crystallisation cycles. The formalization of the proposed model has been based on the multivariable approach which considers environmental data (such as temperature, solar radiation, wind speed, rain quantity, relative humidity), characteristic inflection points of specific salts and stone features derived from laboratory characterizations (including mechanical properties, porosity, and mineralogical composition). Modeling results have been compared with experimental data elaborations acquired by monitoring a semi-confined archaeological site situated in the city of Cagliari (Munatius Irenaus cubicle), revealing substantial alignment in the degradation kinetics trends. Moreover, the achieved outcomes show the remarkable capability to identify salt crystallisation phenomenon type (efflorescence or subflorescence).

Diabetes is becoming more and more dangerous, and the effects continue to grow due to the population's ignorance of the seriousness of this phenomenon. The studies that have been carried out have not been able to follow the phenomenon more precisely, which has led to the use of the fractional derivative tool, which has a very great capability to study real problems and phenomena but is somewhat limited on nonlinear models. In this work, we will develop a new fractional derivative model of a diabetic population, the Takagi–Sugeno fractional fuzzy model, which will enable us to study the phenomenon with these nonlinear terms in order to obtain greater precision in the results. We will study the existence and uniqueness of the solution using the Lipschizian theorem and then turn to the new fuzzy model, which leads us to four dynamical systems. The interpretation results show the quality of fuzzy membership in tracking the malleable phenomena of nonlinear terms existing in the system. [ABSTRACT FROM AUTHOR]

A large number of real-world problems from various fields of human activity can be reduced to optimal partitioning-allocation problems with the purpose of minimizing the partitioning quality criterion. A typical representative of such problem is an infinite-dimensional transportation problem and more generalized problems—infinite-dimensional problems of production centers placement along with the partitioning of the area of continuously distributed consumers with the purpose of minimizing transportation and production costs. The relevant problems are characterized by some kind of uncertainty level of a not-probabilistic nature. A method is proposed to solve an optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. The method is based on the synthesis of the methods of fuzzy theory and optimal partitioning-allocation theory, which is a new science field in infinite-dimensional mathematical programming with Boolean variables. A theorem was proved that determines the form of the optimal solution of the corresponding optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. An algorithm for solving fuzzy partitioning-allocation problems is proposed, which is based on the proved theorem and on a variant of Shor's r-algorithm—a non-differential optimization method. The application of the proposed method is demonstrated on model tasks, where the coefficient of mistrust is integrated to interpret the obtained result—the minimum value of the membership function, which allows each point of the set partition to be assigned to a specific fuzzy subset. [ABSTRACT FROM AUTHOR]

In this study, a fuzzy model is presented for predicting the possibility of degradation due to salt crystallisation cycles. The formalization of the proposed model has been based on the multivariable approach which considers environmental data (such as temperature, solar radiation, wind speed, rain quantity, relative humidity), characteristic inflection points of specific salts and stone features derived from laboratory characterizations (including mechanical properties, porosity, and mineralogical composition). Modeling results have been compared with experimental data elaborations acquired by monitoring a semi-confined archaeological site situated in the city of Cagliari (Munatius Irenaus cubicle), revealing substantial alignment in the degradation kinetics trends. Moreover, the achieved outcomes show the remarkable capability to identify salt crystallisation phenomenon type (efflorescence or subflorescence). [ABSTRACT FROM AUTHOR]

Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPSs). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents a new approach for three-dimensional fuzzy modeling using Simultaneous Perturbation Stochastic Approximation (SPSA) for nonlinear DPSs. The Affinity Propagation clustering approach is utilized to determine the optimal number of fuzzy rules and construct a collection of preceding components for three-dimensional fuzzy models. Fourier space base functions are used in the resulting components of three-dimensional fuzzy models, and their parameters are learned by the SPSA algorithm. The proposed three-dimensional fuzzy modeling technique was utilized on a conventional DPS within the semiconductor manufacturing industry, with the simulation experiments confirming its efficacy. [ABSTRACT FROM AUTHOR]

Mathematical models, especially complex nonlinear systems, are always difficult to analyze and synthesize, and researchers need effective and suitable control methods to address these issues. In the present work, we proposed a hybrid method that combines the well-known Takagi-Sugeno fuzzy model with wavelet decomposition to investigate nonlinear systems characterized by the presence of mixed nonlinearities. Here, one nonlinearity is super-linear and convex, and other is sub-linear, concave, and singular at zero, which leads to difficulties in the analysis, as is known in PDE theory. Linear and polynomial fuzzy models were combined with wavelets to ensure an improvement in both methods for investigating such problems. The results showed a high performance compared with existing methods via error estimates and Lyapunov theory of stability. The model was applied to a prototype nonlinear Schrodinger dynamical system. [ABSTRACT FROM AUTHOR]

A flood stands as one of the most common natural occurrences, often resulting in substantial financial losses to property and possessions, as well as affecting human lives adversely. Implementing measures to prevent such floods becomes crucial, offering inhabitants ample time to evacuate vulnerable areas before flood events occur. In addressing the flood issue, numerous scholars have put forth various solutions, such as the development of fuzzy system models and the establishment of suitable infrastructure. However, when applying a fuzzy system, it often results in a loss of interpretability of the fuzzy rules. To address this issue effectively, we propose to reframe the optimization problem by incorporating stage costs alongside the terminal cost. Results show the proposed model called hybrid fuzzy logic and neural networks (NNs) can mitigate the loss of interpretability. Results also show that the proposed method was employed in a flood early detection system aligned with integrating into Twitter social media. The proposed concepts are validated through case studies, showcasing their effectiveness in tasks such as XOR-classification problems. [ABSTRACT FROM AUTHOR]

In this study, a fuzzy model has been proposed for the control of the degradation process in Cultural Heritage. Specifically, the focus has been on the Nuragic building, Genna Maria, in Villanovaforru, Sardinia. Environmental data and material properties were considered to formalize inferences between different variables of the model. The results highlight the possibility of significant decay processes, such as salt crystallization and freeze-thaw cycles, estimated for all months of the year. A comparison between model elaborations and experimental data (material characterization, environmental data, accelerated ageing) demonstrates the reliability of the proposed procedure.

Mathematical models, especially complex nonlinear systems, are always difficult to analyze and synthesize, and researchers need effective and suitable control methods to address these issues. In the present work, we proposed a hybrid method that combines the well-known Takagi-Sugeno fuzzy model with wavelet decomposition to investigate nonlinear systems characterized by the presence of mixed nonlinearities. Here, one nonlinearity is super-linear and convex, and other is sub-linear, concave, and singular at zero, which leads to difficulties in the analysis, as is known in PDE theory. Linear and polynomial fuzzy models were combined with wavelets to ensure an improvement in both methods for investigating such problems. The results showed a high performance compared with existing methods via error estimates and Lyapunov theory of stability. The model was applied to a prototype nonlinear Schrödinger dynamical system.

The relevance of the topic under consideration on the development of universal applied methods for assessing the effectiveness and efficiency of industrial policy in the military-industrial complex (hereinafter - MIC) is due to its high practical significance in the context of the challenges of technological development associated with digitalization and informatization. It is also due to the peculiarities of the current stage of Russia’s economic development in the conditions of financial and technological sanctions from Western states. The purpose of the study is to develop the main provisions of a methodological approach to assessing the effectiveness of industrial policy in the defense industry based on the use of economic-mathematical modeling apparatus, which involves the construction of neural networks based on fuzzy logic. In the course of the research the authors have solved the following problems: mathematically formalized the object of analysis; developed an algorithm for determining the effectiveness of industrial policy in the MIC using neural networks; formalized the model for assessing the effectiveness of such industrial policy based on fuzzy sets; proposed a system of indicators for assessing industrial policy in the MIC; determined the sequence of actions in the construction the fuzzy model for assessing the effectiveness of industrial policy in the MIC by means of Fuzzy Logic on the software platform MatLab.

Groundwater is the main source of drinking water in the northern Dezful-Andimeshk plain, Iran which has become saline in some cases. To check the drinking quality of groundwater, in October 2019, 16 samples were taken from different parts of the plain. The samples were analyzed for the concentration of major cations, anions, and nitrates.Two methods were used to evaluate water quality: the Schoeller deterministic method and the Fuzzy logic. The Schoeller method categorized water quality from good to bad and determined that 56.81, 20.83, 18.77, and 3.57% of the area had good, acceptable, inappropriate, and bad water quality, respectively. On the other hand, the Fuzzy method showed that 21.60% of the area had desirable groundwater with a confidence level of 70-81, 75.23% had acceptable water quality with a confidence level of 32-70, and 3.69% had non-acceptable quality with a confidence level of 20-22%. The Fuzzy method was found to be better in evaluating water quality as it provided a more comprehensive, accurate, and efficient assessment by covering uncertainties better.

This article examines the problems of optimizing the production process of stable gasoline based on models of the object, which is the target product of a primary oil refining installation, and proposes approaches to their effective solution. Currently, solving the problem of optimizing the main processes at a primary oil processing plant in the oil refining industry is one of the most pressing scientific and production problems, as it ensures the efficiency of further deep processing of the resulting petroleum products. In this regard, this study formulates a mathematical formulation of the problem of optimizing the production process of stable gasoline - the target product of the primary oil refining installation of the Atyrau Oil Refinery, characterized by uncertainty, and proposes a heuristic approach for its effective solution in a fuzzy environment. As a result of the research, mathematical models of complex objects characterized by ambiguity of some parameters, such as the stabilization column of a primary oil treatment plant, were developed, and on their basis a heuristic approach was developed that effectively optimizes the operation of the object in conditions of uncertainty. Models that determine the volumes of gasoline and gas from the stabilization column depending on the input operating parameters of the column are determined on the basis of experimental statistical data and the systematic application of the approach of sequential sequential connection of regressors and the least squares method. Fuzzy models that evaluate vaguely described quality indicators of stable gasoline are synthesized on the basis of expert assessment methods, fuzzy sets and modified methods of sequential inclusion of regressors, least squares. Based on the obtained models of the stabilization column, a heuristic algorithm has been developed for the effective optimization of the production process of stable gasoline under conditions of fuzzy conditions, based on a modification and combination of the principles of Pareto optimality and the ideal point for working in a fuzzy environment. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to address the challenge of designing finite-time fault-tolerant control mechanisms for nonlinear flexible spacecraft systems, which are particularly vulnerable to randomized actuator faults. Diverging from traditional methodologies, our research harnesses the capabilities of the Takagi–Sugeno (T–S) fuzzy framework. A unique feature of our model is the representation of actuator failures as stochastic signals following a Markov process, thereby offering a robust solution for addressing timeliness concerns. In this paper, we introduce a generalized reciprocally convex inequality that includes adjustable parameters, broadening the scope of previous results by accommodating them as special cases. Through the amalgamation of this enhanced inequality and flexible independent parameters, we propose an innovative controller design strategy. This approach establishes a stability standard that guarantees mean-square H ∞ performance. In order to validate the efficacy of the suggested strategy, we present a numerical illustration involving a nonlinear spacecraft system, showcasing the practical advantages and feasibility of our proposed technique. [ABSTRACT FROM AUTHOR]

The growth in the number of people suffering from eye diseases determines the relevance of research in the field of diagnosing retinal pathologies. Artificial intelligence models and algorithms based on measurements obtained via electrophysiological methods can significantly improve and speed up the analysis of results and diagnostics. We propose an approach to designing an artificial intelligent diagnosis system (AI diagnosis system) which includes an electrophysiological complex to collect objective information and an intelligent decision support system to justify the diagnosis. The task of diagnosing retinal diseases based on a set of heterogeneous data is considered as a multi-class classification on unbalanced data. The decision support system includes two classifiers—one classifier is based on a fuzzy model and a fuzzy rule base (RB-classifier) and one uses the stochastic gradient boosting algorithm (SGB-classifier). The efficiency of algorithms in a multi-class classification on unbalanced data is assessed based on two indicators—MAUC (multi-class area under curve) and MMCC (multi-class Matthews correlation coefficient). Combining two algorithms in a decision support system provides more accurate and reliable pathology identification. The accuracy of diagnostics using the proposed AI diagnosis system is 5–8% higher than the accuracy of a system using only diagnostics based on electrophysical indicators. The AI diagnosis system differs from other systems of this class in that it is based on the processing of objective electrophysiological data and socio-demographic data about patients, as well as subjective information from the anamnesis, which ensures increased efficiency of medical decision-making. The system is tested using actual data about retinal diseases from the Russian Institute of Eye Diseases and its high efficiency is proven. Simulation experiments conducted in various scenario conditions with different combinations of factors ensured the identification of the main determinants (markers) for each diagnosis of retinal pathology. [ABSTRACT FROM AUTHOR]

Based on accessible data such as the death registry, this work develops a methodology to provide an estimate of the number of patients and the level of aggravation of their pathologies due to delays in treatment. Firstly, for a given pathology, the deaths will be classified by the most common causes of death. The equivalent number of deceased patients can be obtained by adding this information through the Majority Ordered Weighted Average (MA-OWA). This aggregation will allow obtaining matrix C that indicates the incidence of delay in medical healthcare for each cause of death. Next, matrix L has been obtained, showing the nominal level for each type of patient whose pathology has been aggravated due to a delay in medical attention in each period. From matrices L and C, it is possible to obtain the matrix R that shows the fuzzy relationship between them. The worsening patients in a future period can be obtained from matrix L (obtained from matrix C of that future period and the previously calculated matrix R). Finally, an example illustrates the proposed theoretical model. [ABSTRACT FROM AUTHOR]

The multiple motor drives are complex and coupled MIMO nonlinear systems. Due to the complexity of their mathematical models the development of effective control systems is quite complicated task. This paper presents the design of optimal control based on the quadratic optimality criterion for the central section of the continuous production line using soft computer methods when fuzzy model of the controlled system has been used. In this case, the proposed method demonstrates non-analytical design of the optimal parameters of PI controller with emphasis on minimal knowledge on the controlled system. The realized experimental measurements on a continuous line laboratory model confirmed the effectiveness and the good dynamic properties of the optimal continuous line controller and also its applicability to others MIMO nonlinear dynamic systems. [ABSTRACT FROM AUTHOR]

Flying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art. [ABSTRACT FROM AUTHOR]

Abstract Flying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art.

Airborne magnetic particles may be harmful because of their composition, morphology, and association with potentially toxic elements that may be observed through relationships between magnetic parameters and pollution indices, such as the Tomlinson pollution load index (PLI). We present a fuzzy-based analysis of magnetic biomonitoring data from four Latin American cities, which allows us to construct a magnetic index of contamination (IMC). This IMC uses four magnetic parameters, i.e., magnetic susceptibility χ, saturation isothermal remanent magnetization SIRM, coercivity of remanence Hcr, and SIRM/χ, and proposes summarizing the information to assess an area based exclusively on magnetic parameters more easily. The fuzzy inference system membership functions are built from the standardization of the data to become independent of the values. The proposed IMC is calculated using the baseline values for each case study, similar to the PLI. The highest IMC values were obtained in sites close to industrial areas, and in contrast, the lowest ones were observed in residential areas far from avenues or highways. The linear regression model between modeled IMC and PLI data yielded robust correlations of R2 > 0.85. The IMC is proposed as a complementary tool for air particle pollution and is a cost-effective magnetic approach for monitoring areas. [ABSTRACT FROM AUTHOR]

Summary: In this article, the decentralized repetitive tracking controller design for fractional‐order large‐scale Takagi–Sugeno fuzzy system with time delays is developed. We mainly focus on the design of a decentralized repetitive tracking controller based on the Lyapunov stability theory, by which the addressed large‐scale system asymptotically stabilized with H∞$$ {H}_{\infty } $$ performance index. Further, the repetitive control with quantized signal is developed to ensure the good tracking performance with the presence of interconnected model and external disturbances. Specifically, a logarithmic quantizer is used to quantify the control signal which can reduce the data transmission rate in the network. Finally, a numerical example is presented to verify the effectiveness of the proposed controller design technique. [ABSTRACT FROM AUTHOR]

Thus, in recent years, the construction of sites to support crisis management has been high on the agenda of crisis management of Tabriz. One of the things to consider before constructing these databases is study and select a suitable location for the establishment of this type of use.In this study, a multi-location site of crisis management has been studied in Regions 4 and 10 of Tabriz Municipality. In the first instance, was paid to identify and analyze factors influencing site's Site selection. Raster maps were obtained after collection and preparation of the layers, and then weighting parameters was performed using the AHP in software Expertchoice. Next, the information layer, integrated with each other, based on fuzzy models, and finally combining the results of this stage, the options as desired location, were introduced, among their southern part of the four, was identified as the preferred option. Due to the geographic information system, in solving complex urban issues, and facilitate spatial analysis, has been applied, from ability of this system to prepare, integrate and analyze of layers. as a result of this extraction, bakhsh, south of the 4 region, has the address of a garden, which indicates the appointment of a suitable monthly residential property, such as sabz, amuzshi, warzahi, and ... appropriate domestic policies and conditions this is priority to buy for the stability of this water.i am going to the place of my neighbourhood, and iam connected to it, park Hai Amir Kabir. [ABSTRACT FROM AUTHOR]

With the aggravation of the shortage of staffing hospital beds, elective surgeries have to be postponed or even cancelled, which directly affects hospital income and patient health. Therefore, we propose a fuzzy scheduling model based on a patient transfer strategy in the hierarchical diagnosis and treatment system to ensure timely surgery. We propose a risk estimation method based on health failure mode and effect analysis to reduce the transfer risk and represent the surgery duration and the length of stay as fuzzy numbers to deal with the uncertainty. Given that solving the fuzzy model is challenging, we propose an equivalent formulation to transform the fuzzy model into a two-stage mixed integer linear programming (TMIP) model, which can reduce the loss of decision-making information. Finally, the column and constraint generation algorithm is used to solve the TMIP model that is suited for the structure of the main problem and subproblem. The experiment shows that the patient transfer strategy can effectively relieve the hospital bed shortage, and its potential can be further released if the transfer risk can be properly assessed. The proposed method for solving the fuzzy model can reduce the information loss comparing traditional methods. [ABSTRACT FROM AUTHOR]

This article addresses the issue of event‐triggered security control for nonlinear cyber‐physical systems, characterized by Denial‐of‐Service (DoS) attacks, time‐varying delays, and actuator faults. These complexities are captured using the Takagi–Sugeno fuzzy model. Significantly, this study focuses on DoS attacks targeting the controller‐to‐actuator channel, and subsequently introduces a model for actuator faults that considers the prolonged utilization or degradation of component parts. To optimize communication resource allocation, a model‐independent event‐triggered mechanism is proposed. Utilizing the Lyapunov–Krasovskii function approach, the objective of achieving stochastic stability is transformed into a linear matrix inequalities problem, thus deriving a set of sufficient conditions. The efficiency and validity of the proposed algorithms is demonstrated through simulation results. [ABSTRACT FROM AUTHOR]

Floods have been considered the most common natural disaster worldwide in recent years. Flood hazard potential mapping is required for the management and mitigation of flood. Climatic change and the occupation of rivers and drainage have led to floods in arid areas. The objective of this study is to assess and zoning flood risk using the AHP-FUZZY hybrid approach model in the Dashti region of Bushehr Province. In this study, precipitation, elevation, slope, drainage density, land use, geology and soil parameters were used. These parameters have been constructed and classified in the ARC GIS Software; which were weighed according to the AHP method in AHP SOLVER software and the layers were fuzzy using the fuzzy model. Finally, by combining the AHP-FUZZY method, the flood risk assessment and zoning map were obtained.6.5% of the area is in the very high-risk range, and 27.9% is in the high-risk range. Analysis of the final map shows that Khormuj, Sana and Shonbeh are at greater risk of flooding. The combination of the AHP-FUZZY method in previous research has confirmed that this method has great capacity in flood risk assessment and zoning. Therefore, knowing the flood potential of the basin can be effective in formulating crisis management plans when faced with floods. [ABSTRACT FROM AUTHOR]

The risk warning for steady-state power quality in the power grid is essential for its prevention and management. However, current risk warning methods fall short in predicting the power quality trend while accounting for potential risks. Consequently, this study introduces a novel steady-state power quality risk warning method utilizing VMD-LSTM and a fuzzy model. Firstly, a power quality index prediction method based on variational mode decomposition (VMD) and long short-term memory (LSTM) is proposed. This approach significantly enhances prediction accuracy. Secondly, a power quality risk warning method incorporating kernel density estimation (KDE) and a fuzzy model is proposed, which systematically addresses the uncertainty associated with power quality risks. To validate the effectiveness and practicality of the proposed method, experiments are conducted using field monitoring data from a residential load in southern China. The results affirm the reliability and applicability of the proposed method. The simulation results show that the median error of prediction of power quality indexes by the proposed method is 5.03 % during the evaluated time period, and the prediction accuracy is mostly maintained above 90 %.

The paper is devoted to the model-theoretic formalization of the semantic model of the object domain. The article discusses the concept of a fuzzy model, which is a model where the truth function exhibits properties of a fuzzy measure. We demonstrate that a fuzzy model is a generalization of the concept of fuzzification of a precedent (semantic) model to include a countable number of precedents. In practice, it is common to have partial expert knowledge about the set of events in the object domain, making it difficult to immediately describe the fuzzy model. Additionally, since expert valuations are subjective, they may be incorrect and inconsistent with any fuzzy model. In the article, we introduce the concepts of coordinated and locally coordinated valuation of a set of sentences, and provide proofs for interval theorems and an analogue of the compactness theorem.

Diabetes is becoming more and more dangerous, and the effects continue to grow due to the population’s ignorance of the seriousness of this phenomenon. The studies that have been carried out have not been able to follow the phenomenon more precisely, which has led to the use of the fractional derivative tool, which has a very great capability to study real problems and phenomena but is somewhat limited on nonlinear models. In this work, we will develop a new fractional derivative model of a diabetic population, the Takagi–Sugeno fractional fuzzy model, which will enable us to study the phenomenon with these nonlinear terms in order to obtain greater precision in the results. We will study the existence and uniqueness of the solution using the Lipschizian theorem and then turn to the new fuzzy model, which leads us to four dynamical systems. The interpretation results show the quality of fuzzy membership in tracking the malleable phenomena of nonlinear terms existing in the system.

A large number of real-world problems from various fields of human activity can be reduced to optimal partitioning-allocation problems with the purpose of minimizing the partitioning quality criterion. A typical representative of such problem is an infinite-dimensional transportation problem and more generalized problems—infinite-dimensional problems of production centers placement along with the partitioning of the area of continuously distributed consumers with the purpose of minimizing transportation and production costs. The relevant problems are characterized by some kind of uncertainty level of a not-probabilistic nature. A method is proposed to solve an optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. The method is based on the synthesis of the methods of fuzzy theory and optimal partitioning-allocation theory, which is a new science field in infinite-dimensional mathematical programming with Boolean variables. A theorem was proved that determines the form of the optimal solution of the corresponding optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. An algorithm for solving fuzzy partitioning-allocation problems is proposed, which is based on the proved theorem and on a variant of Shor’s r-algorithm—a non-differential optimization method. The application of the proposed method is demonstrated on model tasks, where the coefficient of mistrust is integrated to interpret the obtained result—the minimum value of the membership function, which allows each point of the set partition to be assigned to a specific fuzzy subset.

Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPSs). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents a new approach for three-dimensional fuzzy modeling using Simultaneous Perturbation Stochastic Approximation (SPSA) for nonlinear DPSs. The Affinity Propagation clustering approach is utilized to determine the optimal number of fuzzy rules and construct a collection of preceding components for three-dimensional fuzzy models. Fourier space base functions are used in the resulting components of three-dimensional fuzzy models, and their parameters are learned by the SPSA algorithm. The proposed three-dimensional fuzzy modeling technique was utilized on a conventional DPS within the semiconductor manufacturing industry, with the simulation experiments confirming its efficacy.

Growing older is a phenomenon that is associated with increasingly complex health situations as a result of the coexistence of several chronic diseases. As a result, there is a downward tendency in both older people and their caretakers’ quality of life, which frequently results in frailty. There are numerous solutions available to treat the issue, which primarily affects older people. The basic and most popular imaging method for predicting cognitive impairment is magnetic resonance imaging. Furthermore, few of the earlier models had a definite level of accuracy when diagnosing the condition. Further, there is a critical need to put in place a stronger, more reliable approach to precise prediction. When compared to other procedures, using magnetic resonance images to predict cognitive decline is the safest and most straightforward. The advanced concept for a better optimized strategy to predict cognitive impairment at an early stage is presented in this research. The hybrid krill herd and grey wolf optimization method is offered as a solution to address the challenges in locating the impacted area. In a short amount of time, a significant number of MRI images are analyzed, and the results show a more precise or higher rate of recognition. [ABSTRACT FROM AUTHOR]

Educators across different fields disseminate their knowledge and utilize digital technologies to improve student skills for their careers and sustainability. Students' skills that are improved are verified based on assessment and knowledge application over different circumstances. The article investigates the impact of the effective educator's knowledge assessment and their role in student skill development. The motivation for the research arises from the realization that teachers' knowledge and their capacity for transferring skills and information to students successfully play a significant role in the quality of education. The goal of the study is to develop a GA2M that has been verified and can be used by educators to improve their performance, enhance student's results, and eventually progress educational practices to use Fuzzy methods for reasoning and to include new rules for improving knowledge to bridge the knowledge gap between educators and students' skill growth. It requires a great effect by the educator to enhance their ability over successive performance improvement. This article analyzes the ability for better improvement using the proposed Guided Ability Assessment Model (GA2M). The proposed model discards the negative impact of the ability on students' skill deterioration. Besides, the ratio of skill improvement across various new abilities and exposures is analyzed using Fuzzy inference. This analysis frames the interference using knowledge rules required for different circumstances. These rules are framed using existing skill implications and problem-solving ability. This proposed model proposes new rules for development of various abilities of educators. Based on their current ability, further training process for the educator's skill development is prescribed. Therefore, the inference for fuzzification is performed for the positive impact on students' skill development. If the inference succeeds, the assessment is leveraged between skill guidance and ability. [ABSTRACT FROM AUTHOR]

Entering the coal phase-out era, major priorities of the Greek lignite mining industry are sustainable mine closure, land reclamation, and planning for long-term and sustainable land repurposing scenarios. A holistic and digitally enabled approach to water management could lead to effective strategic planning in the coal phase-out era. This research proposes an integrated approach to evaluate the hydrological profile of a large and complex open pit lignite mining area based on remote sensing data. This framework included processing of satellite images in different bands and an evaluated digital elevation model of an ≈170 km2 area within the Ptolemais Basin, northern Greece, that has extensive surface lignite mining. Several spectral (SMI, RIP, NDVI, NDWI, MNDWI) and topographic (SPI, TWI, LS) indices were investigated, providing details of spatial and temporal variations in the terrestrial water cycle. The analysis permitted the hydrological characteristics of the study area to be mapped with sufficient accuracy. Water bodies, low-flowing streams, water flow direction, vegetation cover, erodible areas, locations with water accumulation, and areas with high soil moisture were identified. A major additional outcome of this research was the construction of flood-susceptibility maps introducing several hydrological parameters in a fuzzy model. The results indicated several areas inside the mining area with high flood susceptibility. These results were subsequently validated through PPC flood event reports during heavy rainfalls and events reported in the National River Basin Management plans. Using high-resolution digital elevation models and remote sensing data processing to identify hazards, like flooding, could improve land use planning and water management strategies. [ABSTRACT FROM AUTHOR]

Introduction. This paper presents a solution for threat assessment of air targets using the fuzzy logic inference method. The approach is based on the Sugeno fuzzy model, which has multiple inputs representing target trajectory parameters and a single output representing the target threat value. A set of IF–THEN fuzzy inference rules, utilizing the AND operator, is developed to assess the input information.Aim. To develop and test an algorithm model to calculate the threat value of an air target for use in real-time automated command and control systems.Materials and methods. An algorithm model was developed using a fuzzy model to calculate the threat value of a target. The model is presented in the form of a flowchart supported by a detailed stepwise implementation process. The accuracy of the proposed algorithm was evaluated using the available toolkit in MATLAB. Additionally, a BATE software testbed was developed to assess the applicability of the algorithm model in a real-time automated command and control system.Results. The efficiency of the proposed fuzzy model was evaluated by its simulation and testing using MATLAB tools on a set of 10 target trajectories with different parameters. Additionally, the BATE software was utilized to test the model under various air defense scenarios. The proposed fuzzy model was found to be capable of efficiently computing the threat value of each target with respect to the protected object.Conclusion. The proposed fuzzy model can be applied when developing tactical supporting software modules for real-time air defense command and control systems.

Precipitation is one of the main elements of the Earth's hydro-climatic cycle and its variability depends on the complex and non-linear relationships between the climate system and environmental factors. Understanding these relationships and doing environmental planning based on them is difficult. Therefore, classifying data and dividing information into homogeneous and small categories can be helpful in this regard. In the present study, an attempt was made to prepare precipitation, altitude, slope, slope direction, and station density data for 3423 synoptic, climatological, and gauge stations in Iran in the 1961-2015 years’ period. These data were entered into fuzzy (FCM), self-organizing map neural network (SOM-ANN) models and precipitation-spatial zoning. The outputs of the two models were compared in terms of accuracy and efficiency. The results obtained from the output of the models have divided the rainfall conditions of Iran into four zones concerning environmental factors. Evaluations also showed that both models had high accuracy in classifying precipitation parameters; However, the fuzzy model has a relative advantage over the neural network model in the accuracy of results.

The primary objective of this paper is to address the challenge of designing finite-time fault-tolerant control mechanisms for nonlinear flexible spacecraft systems, which are particularly vulnerable to randomized actuator faults. Diverging from traditional methodologies, our research harnesses the capabilities of the Takagi–Sugeno (T–S) fuzzy framework. A unique feature of our model is the representation of actuator failures as stochastic signals following a Markov process, thereby offering a robust solution for addressing timeliness concerns. In this paper, we introduce a generalized reciprocally convex inequality that includes adjustable parameters, broadening the scope of previous results by accommodating them as special cases. Through the amalgamation of this enhanced inequality and flexible independent parameters, we propose an innovative controller design strategy. This approach establishes a stability standard that guarantees mean-square H∞ performance. In order to validate the efficacy of the suggested strategy, we present a numerical illustration involving a nonlinear spacecraft system, showcasing the practical advantages and feasibility of our proposed technique.

The growth in the number of people suffering from eye diseases determines the relevance of research in the field of diagnosing retinal pathologies. Artificial intelligence models and algorithms based on measurements obtained via electrophysiological methods can significantly improve and speed up the analysis of results and diagnostics. We propose an approach to designing an artificial intelligent diagnosis system (AI diagnosis system) which includes an electrophysiological complex to collect objective information and an intelligent decision support system to justify the diagnosis. The task of diagnosing retinal diseases based on a set of heterogeneous data is considered as a multi-class classification on unbalanced data. The decision support system includes two classifiers—one classifier is based on a fuzzy model and a fuzzy rule base (RB-classifier) and one uses the stochastic gradient boosting algorithm (SGB-classifier). The efficiency of algorithms in a multi-class classification on unbalanced data is assessed based on two indicators—MAUC (multi-class area under curve) and MMCC (multi-class Matthews correlation coefficient). Combining two algorithms in a decision support system provides more accurate and reliable pathology identification. The accuracy of diagnostics using the proposed AI diagnosis system is 5–8% higher than the accuracy of a system using only diagnostics based on electrophysical indicators. The AI diagnosis system differs from other systems of this class in that it is based on the processing of objective electrophysiological data and socio-demographic data about patients, as well as subjective information from the anamnesis, which ensures increased efficiency of medical decision-making. The system is tested using actual data about retinal diseases from the Russian Institute of Eye Diseases and its high efficiency is proven. Simulation experiments conducted in various scenario conditions with different combinations of factors ensured the identification of the main determinants (markers) for each diagnosis of retinal pathology.

Usually, state-of-charge (SOC) is a time-varying process and its parameters are influenced by temperature distribution. However, almost all existing identification methods regard this process as an invariant system and less consider the influence of temperature distribution. Here, an adaptive method with consideration of temperature distribution is proposed for the estimation of time-varying state-of-charge in lithium-ion battery (LIB). First, a spatiotemporal kernel-local-embedding modeling (STKLLE) method is developed to obtain the temperature distribution. Then, a least squares support vector machine (LS-SVM) is used to build the relation of inherent parameters of the equivalent circuit model (ECM) with temperature distribution, current and SOC. Furthermore, a novel adaptive robust observer is designed to realize the online estimation of SOC. In this observer, the time-varying relations between open circuit voltage (OCV) and SOC are constructed by an online Takagi–Sugeno (T–S) fuzzy model, and the deviation of SOC estimation caused by disturbances is suppressed by the robust law of sliding mode observer (SMO). The effectiveness of the strategy is verified by experiments of the LIB at different conditions. The results demonstrate that the estimated errors under different working rates are constrained in small range (≤ 0.03) for both charging and discharging processes. Besides, the errors for the proposed method are bounded in [− 0.01, 0.02], which presents better model performance compared with the other two commonly used methods. [ABSTRACT FROM AUTHOR]

Fuzzy projection is a mathematical operator inspired by the inverse fuzzy transform that is used to approximate functions. The fuzzy projection is designed such that the coefficients of the linear combination of the basis functions (fuzzy sets in a fuzzy partition) are optimized to obtain the best approximation of the functions from a global perspective, as opposed to the fuzzy transform, where the approximation focuses on fitting functions locally. The aim of this paper is to extend the fuzzy projection to a higher degree, similarly to the fuzzy transform, where the coefficients of the linear combination of the basis functions are expressed by polynomials. In this way, we can significantly improve the quality of the approximation by combining the settings of the fuzzy partition and the degree of polynomnials. In this paper, we show that a higher-order fuzzy projection can be computed using matrix calculus, leading to an easy algorithmization of the method. We also give its approximation properties and its applicability to discrete functions. The usefulness of higher-order fuzzy projection is demonstrated on two tasks, namely continuous function approximation and audio signal compression and decompression, where the results are compared with other relevant methods. [ABSTRACT FROM AUTHOR]

1-Introduction The natural hazards are impossible to avoid, and infrastructure elements and communities cannot be made totally invulnerable. The only viable solution is the complex risk analysis and subsequent development of combination of mitigation and adaptation strategies Floods are the most common natural disaster type worldwide. Between 1995 and 2015, 2.3 billion people were affected by floods, considerably more than any other type of natural disaster event. In general, the Givi Chai basin includes two cities, Khalkhal and Kausar. In recent years, floods have caused damage to gardens and facilities due to spring and summer rains to the extent that the villages that are located under the Sangur Chai basin because of being located at the source of the rivers and mainly in the course of the rivers are affected by floods; also in the whole basin due to droughts and the destruction of vegetation and the increase of man-made areas, the amount of impermeability has increased. Considering that Khalkhal is a mountainous area and is one of the areas sensitive to landslides in Azariaijan region, heavy rainfall and floods can wash away the slopes of this area. 2-Methodology The Givi Chai basin in the south of Ardabil province with an area of 1554 square kilometers is one of the 3rd degree basins of Sefidroud, which is located in the geographical coordinates of 48°4' E longitude to 48°38' E longitude 37°27' N latitude 37°55' N latitude. In order to achieve the goals of this research, firstly, the theoretical foundations and the works done in connection with flood zoning of basins in Iran and outside Iran were examined. Many variables are effective in the flood of basins. In this research, from the 1.100000 geological map, the slope maps were obtained using the digital height model map; the land use map was obtained through the classification of the Maximum Likelihood in ENVI software. In addition, using the digital model of the height of resolution to extract, a drainage density map was prepared using the Line density tool. In this research, using the FAHP fuzzy hierarchical analysis model, Expert CHOISE software was used for weighting the layers, and finally, a map was prepared in the Arc GIS environment using gamma 0.5, 0.7 and 0.9The basin was flooded. 3-Results After preparing the effective factors for zoning the flood potential of the basin, the gamma fuzzy operators of 0.5, 0.7 and 0.9 were used. 37 floods have occurred in the studied basin from 1365 to 1399 in different parts and villages, which caused the destruction of rural houses, gardens, agricultural lands and livestock. In order to determine the final flood map, points were randomly selected on the zoning map, then the correlation level was obtained based on the specified points with each of the criteria in the ARC GIS software. Based on the results obtained from the correlation, the highest correlation level is related to gamma 0.9. Vegetation has the highest correlation of 0.5 and lithology has the lowest correlation with gamma of 0.9 and 0.062. Also, the last flood occurred in the studied basin in the villages of Mustafa Lo, Bolokan, Morestan and Khoda Qeshlagi, which are located in the high-risk zone according to the final flood zoning map. 4-Discussion & Conclusions In this research, using the FAHP model, flood potential zoning has been evaluated in Givi Chai basin. The data of height, slope, lithology, precipitation, vegetation, soil hydrology, distance from the river, drainage density and land use have been used for flood zoning. After preparing the zoning map, the studied basin was divided into five classes based on the severity of flooding: very low, low, medium, high, very high. The results obtained from the flood zoning of Givi Chai basin have shown that the largest area of the studied basin, which is 0.9 gamma scale, has the highest correlation. The strata with very low, low and medium potential, gamma 0.5 0.7, were showed. cover the area of Givi Chai basin. Also, the final flood map of the basin was validated with the villages that have been damaged by floods in recent years. The areas which are more exposed to floods are in the regions that have very low permeability in terms of lithology and hydrology of the soil and in the areas where the vegetation cover as less as. [ABSTRACT FROM AUTHOR]

Evaluative knowledge of an object domain can be represented not only as an objective (statistical) probability, but also as a subjective (expert) probability. Expert evaluations may be incomplete and uncoordinated with each other. An instrument is needed to check the coordination of expert knowledge. The paper presents a formalization of the subjective interpretation of probability in the language of the theory of fuzzy models. This allows us to formulate coordination criteria for evaluative knowledge obtained from experts. Various properties of coordinated evaluative knowledge are described, the concept of a minimal fuzzy model coordinated with a given evaluative knowledge is considered, and the uniqueness criteria for such a model are investigated. [ABSTRACT FROM AUTHOR]

The paper proposes a method of intellectual support for the adoption of logistics decisions on the basis of genetic clustering and fuzzy modeling. The task was set and the solution of the interrelated tasks of dividing the territory of logistics services into zones, determining the required number of vehicles in the distribution of logistics orders by zones, and the assignment of vehicles for the implementation of logistics orders was presented. The use of the proposed method (by the example of online vehicle orders) makes it possible to improve the quality and efficiency of the logistics decisions. [ABSTRACT FROM AUTHOR]

The response spectra defined in seismic design codes include crisp classifications of seismic parameters, which directly affect the spectra's shape and greatly alter seismic design loads. The optimum design phase seismic forces have an important role in the efficiency of the construction costs and structural safety. Various parameters are used to calculate the seismic design forces, especially presented in the codes with earthquake design spectra. This study presents a rule-based fuzzy inference model with fuzzy sets to determine these parameters using fuzzy inference system (FIS) modelling, which is the most appropriate approach among the different alternatives because both the input and output variables have numerical and linguistic uncertainties in the earthquake problem. Using the seismic zone factor of the region and shear wave velocity of the soil profile as inputs, the model generates the seismic coefficients and peak ground acceleration values of the response spectra specified in the Uniform Building Code (UBC, 1997). The response spectra in this code can be easily generated with these seismic coefficients after their fuzzification. Response spectra of twenty-five different sample cases with and without the FIS model are generated, which provide comparisons for the model superiority assessment. Significant differences are observed between the crisp logic and the FIS model-generated spectra. It is suggested that the FIS model can be modified and applied to various parameters to generate response spectra in different seismic design codes. [ABSTRACT FROM AUTHOR]

African swine fever (ASF) is a contagious and lethal hemorrhagic disease with a high case fatality rate. Since 2007, ASF has been spreading into many countries, especially in Europe and Asia. Given that there is no effective vaccine and treatment to deal with ASF, prevention is an important way for a country to avoid the effects of the virus. Australia is currently ASF-free but the disease has been reported in many neighboring countries, such as Indonesia, Timor-Leste, and Papua New Guinea. Therefore, it is necessary for Australia to maintain hyper-vigilance to prevent the ASF introduction. In this paper, we propose the use of fuzzy concepts to establish a fuzzy risk assessment model to predict the ASF introduction risk in Australia. From the analysis, the international passengers (IP) and international import trade (IIT) are concluded as the two main ASF introduction factors based on transmission features and past research. From the established fuzzy risk assessment model based on the analysis of the 2019 and 2020 data, the risks of ASF introduction into Australia are considered to be low. The model further deduced that the Asian region was the major source of potential risks. Finally, in order to validate the effectiveness of the established fuzzy risk assessment model, the qualitative data from the Department for Environment, Food & Rural Affairs of the United Kingdom was used. From the validation results, it has shown that the results were consistent when the same data is adopted, and thus proved that the functionality of the established fuzzy risk assessment model for assessing the risk in Australia.