Fuzzy Model Research Articles

Robust anti-disturbance interval type-2 fuzzy control for interconnected nonlinear PDE systems via conjunct observer

This paper investigates a robust anti-disturbance interval type-2 (IT2) fuzzy control for interconnected nonlinear partial differential equation (PDE) systems subject to parameter uncertainties by the conjunct observer. First, an IT2 fuzzy model is adopted to remodel the target system. Second, a state observer with mismatched premise variables is constructed to solve the problem that the original system and the observer do not share a uniform premise variable. Moreover, a disturbance observer is designed to estimate the unknown external disturbances, which can be modeled by exogenous PDE systems. Then, utilizing the conjunct observation information, an anti-disturbance IT2 fuzzy control strategy is proposed to attenuate the effect of disturbances on the system performance while ensuring that the closed-loop system is stable. Finally, simulation results verify the effectiveness of the proposed method.

Processes monitoring using adaptive confidence limit based on T-S fuzzy model and Luenberger observer

In hazard-sensitive processes, the monitoring upsets and malfunctions correctly is an important challenge to operation safely and enhance the performance. Conventional process monitoring frequently assumes that process data follow only one Gaussian distribution, which generates a constant confidence limit and hence produces a high number of false alarms. However, in fact, industrial processes usually include various operating modes. To ovoid this drawback, the suggested approach employs an adaptive confidence limit (ACL) when a substantial number of false alarms are created. The fundamental concept underlying this study is to extract internally several local linear sub-modes of the monitored variables. In typical operating circumstances, the Gaussian mixture model (GMM) is utilized to extract several local linear sub-modes, followed by fuzzy linearization using the Takagi-Sugeno model, thereafter a bank of Luenberger observers to construct the residual spaces. An abnormal event is detected when the squared prediction error (SPE) is too great or exceeds the adaptive threshold designed to prevent the false alarms. Furthermore, an enhanced contribution plots is effectively used to identify the defective variable.

Expression of Concern for article entitled “Fuzzy modelling and metaheuristic to minimize the temperature of lithium-ion battery for the application in electric vehicles”

Expression of Concern for article entitled “Fuzzy modelling and metaheuristic to minimize the temperature of lithium-ion battery for the application in electric vehicles”

A fuzzy analytic hierarchy process model to enhance energy security: the case of Morocco

Energy security has become increasingly important because of its impact on all sectors of the economy, and even more because of the scarcity of primary energy resources and price fluctuations caused by geopolitical tensions. This has prompted all countries to assess their energy situation and take strategic measures to mitigate energy risks. Accordingly, the aim of this study is to identify the dimensions of energy security with the greatest impact on Morocco's energy security, as well as the priority strategic measures for mitigating energy risk. To this end, we consider the fuzzy analytic hierarchy process (F-AHP) method, which is a multiple criteria decision analysis method, which has been used to obtain results based on a structured evaluation process. The obtained results, following the considered comparison matrix, underscore that availability and resilience dimensions have the greatest impact on the Moroccan energy security, with respective weights of 48% and 28%. Besides, mitigating energy risk in Morocco primarily involves the development of renewable energies compared with seven other proposed measures, along with their degree of priority.

A novel approach to decentralized sampled‐data fuzzy observer design for nonlinear interconnected systems

AbstractThis paper presents the decentralized sampled‐data fuzzy observer design technique for nonlinear interconnected systems based on the Takagi–Sugeno fuzzy model. The interconnected system is considered that the subsystems include uncertain interconnections and are not needed to satisfy stable conditions. Based on the continuous‐time Lyapunov functional approach, the observer design technique is proposed to minimize the interconnection bound to attenuation degree ratio and formulated into the linear matrix inequality formats. Finally, by simulation example, it shows the validity and superiority of the proposed observer design technique.

Fuzzy Fault Detection Observer Design for Unmanned Marine Vehicles Based on Membership-Function-Dependent H∞/H_ Performance

This paper studies the design problem of fault detection (FD) observer for unmanned marine vehicles (UMVs) based on the T-S fuzzy model. Firstly, T-S fuzzy systems are used to approximate the nonlinear dynamics in UMVs. Secondly, to improve the FD performance of UMVs, a new H∞/H_ performance index, which depends on the membership functions, is defined. Then, based on the membership-function-dependent H∞/H_ performance index, a new fuzzy FD observer strategy, where the fuzzy submodels are not all required to be with the same H_ performance index, is developed to detect the sensor fault in UMVs; the corresponding synthesis conditions of the FD observer are derived based on the Lyapunov theory. Different from the conventional FD strategies, in the proposed membership-function-dependent FD method, the fuzzy submodels—which the system always works on—can have a larger H_ performance index, such that the performance of the FD can be improved. In the end, an example is given to show the effectiveness of the presented method.

Analyzing energy transition for industry 4.0-driven hybrid energy system selection with advanced neural network-used multi-criteria decision-making technique

This study aims to select the appropriate renewable energy alternatives for the efficiency of hybrid energy systems to increase energy transition performance. For this purpose, a novel neural network (NN)-based fuzzy decision-making model is constructed that has three different stages. In the first stage, NN-based fuzzy decision matrix is created. Secondly, 6 different variables based on industry 4.0 are weighted with the sine trigonometric Pythagorean fuzzy entropy technique. Additionally, another calculation has been implemented with criteria importance through intercriteria correlation (CRITIC) to identify the consistency of the results. Furthermore, in the third stage, considering 5 different renewable energy alternatives, 10 different combinations are identified for hybrid energy systems. The most effective alternatives are defined by the sine trigonometric Pythagorean fuzzy ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS) method. Moreover, to test the validity of these results, another analysis is conducted using the additive ratio assessment (ARAS) technique. The main contribution of the study is that the optimal renewable energy combination required for an efficient hybrid energy system is determined by performing a priority analysis between the variables. This situation has a significant guiding feature for investors. Similarly, the development of the RATGOS technique both increases the methodological originality of the study and enables more accurate alternative ranking. It is identified that the results of all methods are similar. Therefore, this situation gives information about the coherency and validity of the findings. It is concluded that the most important criterion is real-time capability. It is also denoted that the best combination for hybrid energy systems is Solar-Wind.

Determining the mechanical behavior of thin-walled cylindrical shells of cement composite containing graphene oxide and glass fibers with the help of fuzzy logic model and artificial neural network

Determining the mechanical behavior of thin-walled cylindrical shells of cement composite containing graphene oxide and glass fibers with the help of fuzzy logic model and artificial neural network

Measuring museum sustainability in China: a DSR model-driven approach to empower sustainable development goals (SDGs)

In the past few years, sustainable development has undoubtedly become an essential perspective for museums. Museums have been committed to raising social awareness around ecological conservation and cultural diversity through various exhibitions, education and research projects, and public engagement. However, the concepts, approaches, and practises of “sustainability” differ across nations and regions, and there is a lack of sufficient quantitative research on sustainable development by and within museums. This article aims to establish an evaluation framework and evaluation approach for museum sustainability in China. Its framework is initially based on the DSR model, in which “D” delineates seven deeds of museum visitors, “S” characterizes the status of sustainable practises within museums, and “R” involves presenting sustainability strategies pertinent to Chinese museums by integrating Sustainable Development Goals (SDGs). A fuzzy QFD model is then used to develop an evaluation approach for analyzing the Zhejiang Natural History Museum (ZNHM)’s sustainable development strategies, using data from surveys and in-depth interviews. As a result of this study, significant opportunities for deepening sustainable practises were identified for ZNHM, which include updating the collection system, enhancing professionalism in exhibitions and educational projects, and securing funding policies. ZNHM’s sustainability efforts prioritize enhancing social awareness, and internal reforms related to social value, governance, and professional capabilities. With these foundations in place, international cooperation among museums may be the next step towards a sustainable future.

Optimization of flight conditions based on performance sensitivity analysis for jet transport aircraft

PurposeThis study aims to enhance the fuel efficiency of jet transport aircraft based on mathematical models and flight crew operating manual (FCOM) for the purpose to assist the civil aviation industry in improving flight safety and operational efficiency.Design/methodology/approachThe research applies flight data mining and fuzzy logic modeling technologies to set up lift-to-drag ratio (L/D) models and nine models of thrust, Mach number, engine pressure ratio and fuel flow rate to estimate the deviation of each flight parameter. All performance deviations are calculated based on the values of flight data recorded in the quick access recorder and FCOM at the observed flight conditions. The L/D model can obtain the influence of each flight parameter and estimate the insufficient amount of each parameter by averaging it with the least square method. In the estimation of optimal altitude, nine models are built based on data from FCOM to estimate the optimal altitude and complete comparative analysis of the airspeed, Mach number and fuel flow rate at the optimal altitude.FindingsAnalyze 11 relevant parameters from the sensitivity derivative of L/D model to obtain how each parameter affected fuel consumption and explore the causes of additional fuel consumption. Complete the estimation of the optimal cruise altitude of the aircraft, and calculate the comparative analysis of the altitude, speed, Mach number and other parameters with the sensitivity derivative of the L/D. The estimation of the optimal cruise altitude of the aircraft can meet the analysis of the sensitivity derivative.Research limitations/implicationsThis study is to enhance the fuel efficiency of jet commercial transport based on mathematical model and FCOM. FCOM is required to conduct this study. The estimation of the optimal cruise altitude through the nine models of the aircraft could meet the analysis of the sensitivity derivative.Practical implicationsThe object of present research is to demonstrate the effectiveness of optimization of flight conditions through model analysis to get knowledge of the effects of each influencing flight variable to L/D for future flight operations’ reference.Social implicationsThe model-based derivative analysis had the ability to perform derivative prediction analysis on any input parameters, more flight parameters could be optimized in future research to help airlines improve flight safety and operational efficiency.Originality/valueThe present enhancement method of fuel efficiency is an innovation to examine the abnormal aircraft performance and its flight operations, thereby to explore the causes of additional fuel consumption. The present method can become an auxiliary tool for flight operations quality assurance to improve fuel efficiency for the airlines.

Using Mental Modeling to Study the Management of Used Cooking Oil in Kuala Nerus

Recycling of used cooking oil is an effort by collecting, processing and reusing solid waste or materials that have been used for the purpose of environmental conservation. This study was conducted to identify the level of knowledge and awareness of used cooking oil management among the community in the Kuala Nerus district, Terengganu. The study involved 30 respondents in the community, namely traders, used cooking oil wholesalers, households, students and the government. The study used a mixed method, i.e. qualitative and quantitative research using fuzzy cognitive modelling including individual mental model modeling. The results of the study showed that the knowledge of cooking oil waste recycling among the respondents was still at a moderate level. The results of the study found that for the aspect of qualitative analysis, there were four themes that emerged, namely knowledge, awareness, attitude and culture. While descriptive analysis identifies the most dominant relationship strength is the theme of awareness. The recommendations in this study are necessary to improve the management of used cooking oil in Kuala Nerus through a comprehensive and integrated approach. This study proposes a legal approach, encourages the expansion of this study to a higher level and improves the provision of used cooking oil management infrastructure.

A New Fuzzy MCDM Model to Select Wireless Network Types in Healthcare Facilities

In recent years, there has been a noticeable rise in the need for wireless network connection in healthcare facilities. This is a result of the requirement for immediate access to patient data as well as the growing number of medical devices that are internet-connected. To guarantee dependable and secure communication as well as support for diverse healthcare applications and services, it is essential to choose the right wireless network type. However, the selection procedure can be difficult because it includes a number of variables that are frequently arbitrary and subjective. The difficulties of choosing the best wireless network type to be used in healthcare facilities are addressed in this research by the introduction of a new fuzzy model for multi-criteria decision-making (MCDM). The approach considers several evaluation factors, including patient cost, patient privacy, patient safety, and patient comfort and convenience. The model uses a fuzzy analytic hierarchy process (FAHP) to compute the weights of these criteria. In addition, the model uses fuzzy methods like fuzzy ARAS, fuzzy EDAS, and fuzzy TOPSIS to rank the different kinds of wireless networks according to their performance. It is clear from the results that the suggested model can effectively and dependably assess different alternatives. Additionally, it provides useful and practical advice on how to choose the right wireless network types for medical facilities. The best feature is that this approach is easily adaptable to other healthcare industry decision-making procedures.

A Multimodal Fuzzy Approach in Evaluating Pediatric Chronic Kidney Disease Using Kidney Biomarkers.

Chronic kidney disease (CKD) is one of the most important causes of chronic pediatric morbidity and mortality and places an important burden on the medical system. Current diagnosis and progression monitoring techniques have numerous sensitivity and specificity limitations. New biomarkers for monitoring CKD progression have been assessed. Neutrophil gelatinase-associated lipocalin (NGAL) has had some promising results in adults, but in pediatric patients, due to the small number of patients included in the studies, cutoff values are not agreed upon. The small sample size also makes the statistical approach limited. The aim of our study was to develop a fuzzy logic approach to assess the probability of pediatric CKD progression using both NGAL (urinary and plasmatic) and routine blood test parameters (creatinine and erythrocyte sedimentation rate) as input data. In our study, we describe in detail how to configure a fuzzy model that can simulate the correlations between the input variables ESR, NGAL-P, NGAL-U, creatinine, and the output variable Prob regarding the prognosis of the patient's evolution. The results of the simulations on the model, i.e., the correlations between the input and output variables (3D graphic presentations) are explained in detail. We propose this model as a tool for physicians which will allow them to improve diagnosis, follow-up, and interventional decisions relative to the CKD stage. We believe this innovative approach can be a great tool for the clinician and validates the feasibility of using a fuzzy logic approach in interpreting NGAL biomarker results for CKD progression.

Suitability Evaluation of Urban Underground Space Development: A Case Study of Qingdao City

Urban underground space, as an underutilized land resource, holds tremendous potential and value. Efficient and rational development and utilization of this resource are key to addressing current urban challenges. This study takes the main urban area of Qingdao City as an example and establishes a comprehensive evaluation system for the suitability of urban underground space development at different depth levels through the integration of geological, hydrological, and urban planning factors. By utilizing the Analytic Hierarchy Process to assign weights to evaluation criteria within the system, both a multi-objective linear weighting function model and a fuzzy comprehensive evaluation model are employed to assess the suitability of underground space development. The results delineate the distribution of underground space development suitability within the study area. Comparative analysis of the two models reveals that the fuzzy comprehensive evaluation model offers a more detailed and comprehensive reflection of the complexity and diversity of underground space development, providing forward-looking and insightful evaluation results for urban planning and development. The evaluation indicates that certain streets within the main urban area of Qingdao exhibit excellent prospects for underground space development.

High-density sampling of soil heavy metals in the upper Bailang River basin: contamination characteristics, sources, and source-oriented health risk assessment.

Heavy metals (HMs) seriously harm soil environment and threaten crop quality and human health. The aim of the study was to investigate the characteristics, quantify the sources and assess the risks of HMs in soil of upper Bailang River Basin (UBRB). The results indicated that the soils in UBRB were at a non-polluted level and posed a low ecological risk to the environment as a whole. The main pollutants were Ni and Cr obtained by indices Pi and Igeo. Based on the consideration of toxicity, the fuzzy comprehensive evaluation model and Ei index revealed that Hg and Cd were dominating pollutants and ecological risk factors of soil in UBRB. The positive matrix factorization model ascertained five potential sources of soil HMs, namely, plastic processing, energy activities, parent material, transportation and agriculture mixed source and industrial manufacturing, with contribution rates of 17%, 7%, 15%, 29% and 32%, respectively. Natural source primarily determined the non-carcinogenic risk for all populations, accounting for about 43% of the total risk. Industrial manufacturing mainly determined the carcinogenic risk, accounting for about 45%. For adults, the risk was acceptable for most of the sample points. For children, potential non-carcinogenic risks were present in 13.19% of the sample sites, which were mainly located in the west, and unacceptable carcinogenic risks were present in 57.21% of the sample sites, which were mainly concentrated in the western and central parts.

Модель процесса формирования каталога сценариев для анализа и оценки функциональной безопасности автоматизированных систем вождения автомобилей

In the development of highly automated and unmanned vehicles, the auto industry is faced with a new set of problems associated with the need to standardize elements of automated driving systems and determine the rules for their testing and certification. Due to the acceleration of scientific and technological progress and the development of new technologies, previously used standards began to become obsolete as the practice of their application accumulated, and sometimes even before the approval of a particular technical regulation. In these conditions, a promising technical and legal solution may be the use of fuzzy logic in tools for aggregation and management of expert knowledge in the processes of certification and testing of highly automated vehicles and elements of their automated control systems. Fuzzy decision-making models use typical fuzzy situations that form catalogs of scenarios for testing vehicles and their systems. In this way, an expert system knowledge base can be formed in which knowledge engineers apply a set of test scenario or experiment parameters for testing and simulation. Determining the parameters of a new scenario, their similarity with previously formalized scenarios and the decision to include a particular scenario in the scenario catalog remains with the experts. The approaches used at the present stage to the formalization of expert knowledge are not acceptable for creating knowledge bases controlled by big data management systems or artificial neural networks, which are in the near future in the development of expert systems. The article proposes a method by which a knowledge engineer administering an expert system can automate the creation of a catalog of “fuzzy” test scenarios and simulation modeling of systems, by automatically searching for the maximum value of the states of these systems belonging to maps and catalogs of scenarios, with a probability specified by the expert, with using machine learning methods.

Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press

With the rapid development of the manufacturing industry toward intelligence and flexibility, traditional mechanical presses are unable to meet the increased stamping requirements due to the difficulties in achieving variable speed control and changing slide motion trajectories. Servo presses, driven directly by servo motors, can realize the flexible control of press movement and have become the trend in the industry for future development. The independent research and development of servo press control systems has become a popular topic and challenge in the domestic press industry. This paper proposes a new type of press transmission mechanism suitable for high-precision stamping, analyzes the working principle of its transmission mechanism, and provides a detailed analysis and discussion on the main research methods of fuzzy intelligent control for servo presses, including fuzzy model and basic control algorithms, fuzzy modeling and control of nonlinear systems, fuzzy predictive control, and stability analysis of fuzzy control systems. Based on the principle of the control scheme, a closed-loop control system block diagram and mathematical model for servo high-precision presses are established. Taking the physical prototype parameters of the STSZG1-250 as an example, the control system is dynamically simulated and verified using Simulink modules. Finally, their role and effects in intelligent control, and prospects for the development of fuzzy intelligent control, are discussed.

An intelligent design system for tailored metamaterial properties

Metamaterials are widely studied for their ability to carry human designs and realize exotic, specific physical properties. However, the existing metamaterial design methods are highly dependent on the experience and expertise of the designers. Unlocking the potential of metamaterials requires efficient design methods accessible beyond expert users. This work presents a novel intelligent design system (IDS) for fabricating metamaterials with designated mechanical properties without extensive expertise. The IDS leverages a combined approach of particle swarm and moving morphable components topology optimization, capitalizing on swarm intelligence for efficient material structure generation. The proposed IDS successfully generates tailored metamaterial matrices exhibiting desired elastic properties like Poisson's ratio and elastic modulus for orthotropic and isotropic materials. Innovative development of fuzzy models is achieved to optimize convergence mapping of isotropic mechanical metamaterials for user guidance. Notably, it achieves this even without requiring an initial design, completing the process within minutes and offering seamless integration with MATLAB for portability. From the pre-determination of convergence before design to the export of fabrication models after design, IDS provides a new path to designing metamaterials without needing research experience. This comprehensive, user-friendly system holds immense potential for expansion into 3D design and various metamaterial classes, acting as a valuable bridge for non-experts to achieve metamaterial design.

Fuzzy Cluster Pitch Synthesis System for the Violin Sound with Machine Learning

Pitch synthesis with violin sound involves the generation of musical pitches using technology to mimic the distinctive tonal characteristics of a violin. This process typically employs digital signal processing techniques to recreate the timbre, articulation, and nuances of a real violin. Advanced algorithms analyze and model the acoustic properties of a violin sound, allowing for the synthesis of realistic pitch variations and expressive qualities. Whether utilized in electronic music production, virtual instruments, or sound design, pitch synthesis with violin sound aims to emulate the rich and complex sonic palette of the violin, offering musicians and composers versatile tools for creative expression and sonic exploration. In this paper proposed Fuzzy Pitch Clustering Machine Learning (FPC-ML) for the violin Music Pitch Synthesis using Machine Learning. The proposed FPC-ML model uses the Fuzzy Clustering model for the estimation of pitches in the violin music signal. Based on the Fuzzy clustering model membership degree is computed for the proposed FPC-ML for the estimation of the pitch in the violin music. With the estimation of linguistic variables, clustering is performed in the Music signal for the computation of pitches. With the estimated pitches in the violin music, the features are trained in the machine learning model for the classification and estimation of features in the Violin Music. Simulation analysis demonstrated that the proposed FPC-ML model computes the features of the Violin Music Pitch values based on the estimated clustering values synthesis performed for the classification of the Violin Music signal. The proposed FPC-ML technique achieves an accuracy value of 0.98 for the violin signal with an iteration of 20. With the increase in several iterations and epoch, the accuracy of the FPC-ML model is further increased for the synthesis of the Violin Music.

Generation of Graphic Design Color Schemes Based on CMYK Color Model and Corrosion Algorithms

Graphic design colour schemes play a fundamental role in shaping the visual identity and impact of a design. This paper proposed an efficient Feature Extraction Madhami Fuzzy Clustering Probabilistic Corrosion (FEMFcPC) for the graphic design. The proposed FEMFcPC model comprises the CMYK color scheme for the estimation of the features in the images. With the estimation of the FEMFcPC model corrosion algorithm is implemented for the computation of the graphic design features. Once the features are estimated the related features for the graphic design are designed with the mamdhami fuzzy clustering model. The estimated features are clustered with the CMYK color schemes. The proposed FEMFcPC model estimates the patterns and relationships among graphic designs to provide valuable insights for design categorization and recommendation systems. A dataset comprising various graphic design examples with their corresponding CMYK color schemes is utilized for analysis. Through the application of the FEMFcPC algorithm, clustering results are obtained, revealing distinct grouping patterns and similarities among the designs. The simulation results stated that the proposed FEMFcPC model achieves a significant color scheme for the graphic design. Through the clustering process, the proposed FEMFcPC model with the values of the probabilistic features for the graphic design. The Key findings include the identification of dominant clusters, even distribution of designs across multiple clusters, detection of outliers, and assessment of cluster density. These insights offer significant implications for design categorization, recommendation systems, and targeted marketing strategies within the graphic design domain. The study contributes to advancing our understanding of graphic design analysis and provides a foundation for future research in this field.