Fuzzy Domain Research Articles

A Review of Image Denoising Using Fuzzy and Wiener Filters in the Wavelet Domain

This paper focuses on image denoising using fuzzy wavelet domain transforms, reviewing recent advancements in this area. Wavelet transforms have become a powerful tool in image denoising, with one of the most widely used techniques involving thresholding wavelet coefficients. The paper proposes a hybrid denoising method that combines the wavelet transform, median filtering, and nonlinear diffusion. Additionally, a novel fuzzy filter is introduced to reduce additive noise in digital color images. Two distinct image denoising techniques are discussed: the first employs an Asymmetrical Triangular Moving Average Filter (TMAV) with a HAAR wavelet transform, while the second utilizes an Asymmetrical Triangular Median Filter (TMED) with the HAAR wavelet transform.

An artificial bee colony optimization algorithms for solving fuzzy capacitated logistic distribution center problem

This paper presents a methodological approach to solving the fuzzy capacitated logistic distribution center problem, with a focus on the optimal selection of distribution centers to meet the demands of multiple plants. The distribution centers are characterized by fixed costs and capacities, while plant demands are modeled using fuzzy triangular membership functions. The problem is mathematically formulated by converting fuzzy demands into crisp values, providing a structured framework for addressing uncertainty in logistic planning. To support future research and facilitate comparative analysis, 20 benchmark problems were generated, filling a gap in the existing literature. Three distinct artificial bee colony algorithm variants were hybridized with a heuristic: one using the best solution per iteration, another incorporating chaotic mapping and adaptive procedures, and the third employing convergence and diversity archives. An experimental design based on Taguchi's orthogonal arrays was employed for optimizing the algorithm parameters, ensuring systematic exploration of the solution space. The developed methods offer a comprehensive toolkit for addressing complex, uncertain demands in logistic distribution, with code provided for reproducibility.Key contributions include:•Development of a fuzzy model for the selection of distribution centers with fixed costs and capacities under uncertain plant demands.•Generation of 20 benchmark problems to advance research in the fuzzy capacitated logistic distribution center problem domain.•Integration of a heuristic approach with three distinct ABC algorithm variants, each contributing unique methodological insights.

Exploring Profit Opportunities in Intuitionistic Fuzzy Metric Spaces via Edelstein Type Mappings

This paper establishes a break-even point theorem concerning a set of mappings adhering to an Edelstein-type contractive criterian within intuitionistic fuzzy metric domains. It explores the break-even analysis within a straightforward total cost-revenue model applicable to dynamic businesses. Utilizing the coincident point theorem within intuitionistic fuzzy metric space, the study demonstrates the inclination of profit-sensitive (or loss-sensitive) dynamic businesses towards their respective break-even points.

Fuzzy Rule-Based Heterogeneous Domain Adaptive: A Case Study of Particle Motion in Flow Field from Simulation to Experiment

Fuzzy Rule-Based Heterogeneous Domain Adaptive: A Case Study of Particle Motion in Flow Field from Simulation to Experiment

Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain.

Solving Fuzzy Assignment Problem of Pythagorean Fuzzy Numbers Exploiting a Software Tool

A fresh approach to computing assignment issues in the pythagorean fuzzy domain is the focus of this body of work. Pythagorean fuzzy numbers are utilised in this work for the sake of analysing working hours and working costs. For converting fuzzy sets integers into a crisp number, a new ranking process is used. Optimal solutions have to be found by using software techniques for minimization, maximization and unbalanced assignment problems. In the end, numerical examples are being solved by using the proposed technique.

Reinforced fuzzy domain adaptation: Revolutionizing data-unaccessible rotating machinery fault diagnosis across multiple domains

Existing domain adaptation methods typically require access to raw data from both the source and target domains, increasing the risk of data leakage. In light of this, a fuzzy domain adaptation-based rotation machinery fault diagnosis method that does not require access to data is proposed in this paper. Specifically, during the source domain training phase, all source models are simultaneously trained by contributing model parameters. The developed joint training strategy significantly enhances the multi-domain performance of each private source model. Simultaneously, a model incorporating a fusion fuzzy strategy is constructed to acquire fuzzy rules for the source domain samples and anchor points corresponding to each fault mode. During the target domain adaptation phase, firstly, under the setting of frozen fuzzy rules, the source feature extractor is retrained using target data. Subsequently, an anchor point alignment strategy is introduced to transfer diagnostic knowledge between domains. Finally, a filtering strategy is devised to obtain high-confidence pseudo-labels for the target domain, coupled with a self-supervised learning strategy for the final optimization of the target model. Experimental results demonstrate significant achievements of the proposed method across twelve multi-source cross-domain fault diagnosis cases, with an average diagnostic accuracy of 98.88%. Encouragingly, the proposed method maintains outstanding performance even without accessing source data, outperforming several other methods.

Power Source Converter Based on a Variable-Domain Fuzzy PI Control

The inadequacy of conventional control strategies for multi-phase interleaved parallel circuits in terms of adaptive adjustment makes it difficult to meet the power source design requirements for transient electromagnetic detection systems. This paper introduces a novel approach, the variable-domain fuzzy proportional–integral (PI) adaptive control strategy. This strategy dynamically adjusts the fuzzy domain in real time based on the input error magnitude, ensuring improved control effectiveness. By leveraging the benefits of both the functional scaling factor and the fuzzy reasoning scaling factor, we design scaling factors for the input and output domains to enhance control precision. The focus of this study is on a four-phase interleaved parallel converter, emphasizing the design of the variable-domain fuzzy PI control strategy for the voltage outer loop within the traditional dual closed-loop structure. An experimental prototype with a 220 Vac input, 380 V output, and a power rating of 1000 W is constructed. A comparative analysis between fuzzy control and variable-domain fuzzy PI control is conducted in the voltage outer loop of the dual closed-loop control. Results reveal that dual closed-loop control with variable-domain fuzzy PI control for the voltage outer loop significantly enhances system stability. The startup time to reach the steady state is reduced to 0.632 s, with an overshoot of 28.8 V. Transitioning from 25% load to full load takes only 0.096 s, resulting in a minimal drop of 21.4 V and an overshoot of 13.4 V. Similarly, switching from full load to 25% load in 0.167 s exhibits an overshoot of only 19.6 V. The adaptive regulation capability of the converter is markedly improved, showcasing smaller overshoots and higher-level controlling effectiveness.

Unveiling the intrinsic interactions of science and technology in the fuzzy domain: A visual and quantitative analysis

The progression of the fuzzy domain is propelled synergistically by the evolution of science via academic papers and the advancement of technology through technical patents. However, the intrinsic interrelationships within the science and technology (S&T) interactive landscape of this domain remain largely unknown, as few studies attempt to explore these close interrelationships. To bridge this research gap, based on 161,060 academic publications and 36,693 granted patents, we measured their mutual attribution between S&T from the perspective of textual semantics and structure information on top of a quantitative network monitoring process. The results showed that the fuzzy domain lacked technology-oriented topics in terms of semantic relations and presented an interactive pattern where science was ahead of technology; that is, science worked as an engineer to expand the knowledge boundaries of the research field, and technology existed more as a beneficiary. This article presents cutting-edge insights into the intrinsic S&T interactions of the fuzzy domain in a visual and quantitative approach for the first time and aims to aid researchers in theoretically grasping the S&T innovation laws.

Random generation of linearly constrained fuzzy measures and domain coverage performance evaluation

The random generation of fuzzy measures under complex linear constraints holds significance in various fields, including optimization solutions, machine learning, decision making, and property investigation. However, most existing random generation methods primarily focus on addressing the monotonicity and normalization conditions inherent in the construction of fuzzy measures, rather than the linear constraints that are crucial for representing special families of fuzzy measures and additional preference information. In this paper, we present two categories of methods to address the generation of linearly constrained fuzzy measures using linear programming models. These methods enable a comprehensive exploration and coverage of the entire feasible convex domain. The first category involves randomly selecting a subset and assigning measure values within the allowable range under given linear constraints. The second category utilizes convex combinations of constrained extreme fuzzy measures and vertex fuzzy measures. Then we employ some indices of fuzzy measures, objective functions, and distances to domain boundaries to evaluate the coverage performance of these methods across the entire feasible domain. We further provide enhancement techniques to improve the coverage ratios. Finally, we discuss and demonstrate potential applications of these generation methods in practical scenarios.

Notice of Violation of IEEE Publication Principles: A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale Images

Notice of Violation of IEEE Publication Principles A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale by Swagatam Das, Satrajit Mukherjee, Bodhisattwa Prasad Majumder and Aritran Piplai Submitted to IEEE Transactions on Fuzzy System After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper duplicates significant amounts of content from the original paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: An Optimal Fuzzy System for Edge Detection in Color Images using Bacterial Foraging Algorithm, by Om Verma Submitted to IEEE Transactions on Evolutionary Computation, 12 April 2012 This article presents an optimal edge detection scheme based on the concepts of fuzzy Smallest Univalue Assimilating Nucleus (SUSAN) and the Gravitational Search Algorithm (GSA). Initially, the Univalue Assimilating Nucleus (USAN) area is calculated from the gray levels of every neighborhood pixel of a pixel of interest in the test image. In accordance with the SUSAN principle, the neighborhood is chosen as a circular mask and applied separately on the individual RGB components of the image in case the image is a color image. The USAN area edge map of each component is fuzzified using a Gaussian membership function (used for detecting strong edges) and a bell-shaped function (used for detecting weak edges). Then the entropy and edge sharpness factors are calculated from these fuzzy measures and optimized using GSA by evolving the fuzzifier and the parameters controlling the shape and range of the bell-shaped curve. Adaptive thresholding converts the fuzzy domain edge map to a spatial domain edge map. Finally, the individual RGB edge maps are concatenated to obtain the final image edge map. Qualitative and quantitative comparisons have been rendered with respect to a few promising edge detectors (both traditional as well as state-of-the-art) and also optimal fuzzy edge detectors based on metaheuristic algorithms like Differential Evolution (DE) and Particle Swarm Optimizer (PSO). Extensive comparisons based on several quantitative measures strongly reflect merits of the proposed method.

An Adaptive Fuzzy Logic Control Method for Froth Velocity in the Flotation Process

In the flotation process, too high or too low froth velocity can lead to a reduction in the recovery of useful minerals. However, it is difficult to achieve stable control of froth velocity due to complex process characteristics and fluctuating working conditions. Uncertain feed conditions (feed flow rate, feed grade, and feed concentration) can cause large changes in the desired froth velocity and other uncertain conditions, which affect the accurate evaluation of the velocity state for feedback control. In this paper, an adaptive fuzzy logic control method for froth velocity (AFLC-FV) in the flotation process is proposed. In the method, an adaptive fuzzy quantization function with a dead zone is designed and used instead of the constant fuzzy quantization factor in the fuzzification procedure. Thus, the dynamic real domain of the velocity error can be mapped to a fixed standard fuzzy domain. Meanwhile, the width of the dead zone is adaptively adjusted according to froth mobility to improve steady-state performance. In addition, the velocity trend is extracted and used as an input variable to the controller together with the velocity error. The application results show that, compared with manual control, the AFLC-FV significantly enhances the control accuracy and stability under different ranges of velocity set-points, and effectively improves the mineral recovery by 0.18%.

Application of Variable Theory Domain Fuzzy Control Algorithm for Room Temperature Control

With the development of automatic control in the HVAC industry, more and more buildings have adopted automatic control of air conditioning, HVAC systems, but most of the control results can not be satisfactory. The research object of this paper is the elevator machine room equipped HVAC, through accurate control of that room temperature can greatly reduce the elevator shutdown caused by the action of the temperature protection circuit, so as to improve the safety of elevator operation. This paper gives three different temperature control methods, establishes the math representation of the temperature system in the machine room, specifically designs the classical PID controllers, Fuzzy PID controllers and variable domain Fuzzy PID controller, and simulates and analyzes the three algorithms respectively, The results indicate that in comparing the above three algorithms, the variable domain fuzzy PID algorithm has an adjustment time of 63s, and the overshooting amount of 0.4%, which is the best among the three control algorithms.

Source-Free Multidomain Adaptation With Fuzzy Rule-Based Deep Neural Networks

Unsupervised domain adaptation deals with a task from an unlabeled target domain by leveraging the knowledge gained from labeled source domain(s). Fuzzy system is adopted in domain adaptation to better tackle the uncertainty caused by information scarcity in the transfer. Most existing fuzzy and non-fuzzy domain adaptation methods depend on data-level distribution matching to eliminate domain shift. However, data sharing can trigger privacy concerns. This situation results in the unavailability of source data, wherein most domain adaptation methods cannot be applied. Source-free domain adaptation is then proposed to handle this problem. But existing source-free domain adaptation methods rarely deal with any soft information component due to data imprecision. Besides, fewer methods handle multiple source domains which provide richer transfer information. Thus, in this paper, we propose source-free multi-domain adaptation with fuzzy rule-based deep neural networks (SF-FDN), which takes advantage of a fuzzy system to handle data uncertainty in domain adaptation without source data. To learn source private models with high generality, which is important to collect low noisy pseudo target labels, auxiliary tasks are designed by jointly training source models from multiple domains which share source parameters and fuzzy rules while protecting source data. To transfer fuzzy rules and fit source private parameters to the target domain, self-supervised learning and anchor-based alignment are built to force target data to source feature spaces. Experiments on real-world datasets under both homogeneous and heterogeneous label space scenarios are carried out to validate the proposed method. The results indicate the superiority of the proposed fuzzy rule-based source-free multi-domain adaptation method.

Enhancing Anomaly Detection Models for Industrial Applications through SVM-Based False Positive Classification

Unsupervised anomaly detection models are crucial for the efficiency of industrial applications. However, frequent false alarms hinder the widespread adoption of unsupervised anomaly detection, especially in fault detection tasks. To this end, our research delves into the dependence of false alarms on the baseline anomaly detector by analyzing the high-response regions in anomaly maps. We introduce an SVM-based false positive classifier as a post-processing module, which identifies false alarms from positive predictions at the object level. Moreover, we devise a sample synthesis strategy that generates synthetic false positives from the trained baseline detector while producing synthetic defect patch features from fuzzy domain knowledge. Following comprehensive evaluations, we showcase substantial performance enhancements in two advanced out-of-distribution anomaly detection models, Cflow and Fastflow, across image and pixel-level anomaly detection performance metrics. Substantive improvements are observed in two distinct industrial applications, with notable instances of elevating the image-level F1-score from 46.15% to 78.26% in optimal scenarios and boosting pixel-level AUROC from 72.36% to 94.74%.

Comprehensive multi-benefit planning of sustainable interconnected microgrids

A comprehensive stochastic planning model with multi-objective, integrated sizing and scheduling strategies for distributed energy resources is fabricated and implemented for interconnected grid-tied microgrids in the present work. The optimum capacities of the renewable and dispatchable distributed generators, together with battery energy storage systems in the interconnected microgrids, are ascertained using grey wolf optimizer in the fuzzy domain with the objectives of minimizing the yearly expenses, emissions and energy loss, and maximizing the yearly monetary benefit acquired due to the deferral of network upgrades. The optimal dispatch of multi-energy resources is intended to satisfy price-sensitive, multi-energy demands economically and sustainably under the influence of a price-based demand response strategy. The proposed multi-microgrid planning framework, which is hinged on probabilistic power flow with the probabilistic modelling of renewable and load uncertainties, is targeted to benefit multiple entities, including the utility and microgrid prosumers. The effectiveness of the proposed formulation is demonstrated by the mitigation of the annual expenses, emissions, and energy loss by 51%, 70%, and 56%, respectively, and the deferral of the need for the relevant upgrades by 12 years. Moreover, the performance of the integrated resource allocation and scheduling model is also endorsed using environmental sustainability factors.

Aspect-based sentiment score and star rating prediction for travel destination using Multinomial Logistic Regression with fuzzy domain ontology algorithm

Due to its significant advantages for downstream applications, such as recommender systems, In the context of travel and tourism, user reviews on TripAdvisor have an impact on other travellers' judgments regarding a variety of travel-related issues, including the choice of a vacation spot, lodging, and places to visit. In graded user reviews, the model must specifically forecast the user's review score after receiving the textual review. The purpose of this article is to present a predictive outline for aspect-based extraction and classification that can estimate the users' optimal travel destination. This impression helps travellers in a variety of ways, such as by recommending better locations that also include expensive destinations. The underlying classification algorithm's processing time is significantly impacted by more dimensions. The term “curse of dimensionality” is often used in statistics and machine learning to describe these issues. By projecting the high-dimensional input data into the low-dimensional subspace while roughly maintaining the distance between the data points with a higher probability, the Random Projection (RP) ensemble classifier decreases the complexity of multivariate data. Extract the crucial information from the reviews, then use Glove word vector representation to categorise the relevant emotions. Further, the article proposed Multinomial Logistic Regression (MNLR) with a Fuzzy Domain Ontology (FDO) algorithm for aspect-based sentiment analysis. More intricate aspects than just the products themselves impact how satisfied people are with tourist destinations. The most important factor in evaluating how convenient a tourist route will be is typically the weather. The combined form of predicted sentiment score, start ratings and environment factor has to be calculated to predict the travel destination based on the measurement of personalized search results. The simulation was processed in Python software. The presented work has utilized some performance measures to evaluate the classification model such as F1-score, Recall, Precision, Mean Absolute Error (MAE), Mean Squared Error (MSE), Cohen Score and Matthew Score. The accuracy with GloVe word vector representation was 90% and after the GloVe representation, the classification model accuracy was about 94%. The proposed strategy outperforms in terms of classification accuracy, according to simulated results and analyses of real-world data.

Mixed Correlation Coefficient Between Probability Hesitation Fuzzy Sets and Applications

Probability hesitation fuzzy sets(PHFSs) have been paid increasing attention in the fuzzy domain nowadays, and correlation coefficient is becoming an important research content in fuzzy data analysis, many scholars have been conducted relevant researches on it. However, the existing correlation coefficients between PHFSs have some limitations, such as not considering the number of membership degrees and having counter-intuitive phenomenon to some extent. Therefore, we propose the mixed correlation coefficient between PHFSs. To begin with, we give the concepts of the average, variance, and length ratio of PHFE to illustrate the integrity, distribution, and length. Then, we define the average, variance, and length three correlation coefficients respectively on the above basic concepts. Furthermore, we construct the mixed correlation coefficient through combining these basic correlation coefficients, and extend to weighted form in addition, which addresses the issues of the available correlation coefficients. Finally, we utilize proposed correlation coefficient to analysis problems of data association and decision making. The effectiveness and rationality of the proposed method is illustrated at length.

Riemann Liouville fractional-like integral operators, convex-like real-valued mappings and their applications over fuzzy domain

It is well-known fact that fuzzy number theory is based on the characteristic function but in the fuzzy region, the characteristic function is a membership function that depends upon the interval 01. It means that real numbers and intervals are exceptional cases of fuzzy numbers. By using this approach, another type of Riemann Liouville fractional integral operator has been introduced over a fuzzy domain which is known as Riemann Liouville fractional-like integral operator. These integrals look like Riemann Liouville fractional integral operators but classical integrals are special cases of newly defined integral operators. Some novel versions of these integrals are also obtained and explained by taking the domain's triangular and trapezoidal fuzzy numbers. We also consider the problem of computing scalar-valued. To discuss the application of new integrals, a new class of convex real-valued functions has been introduced, known as convex-like real-valued mappings. With the help of these new concepts, some applications are taken into account. By using convex-like real-valued mappings and newly proposed fractional-like integral operators, the well-known Hermite-Hadamard H.H type and related inequalities are taken into account in this work. Then by defining new differentiable real-valued functions over fuzzy regions, an identity has been obtained and with the support of this identity, certain inequalities are also acquired. Also, we clearly show the important connections of the derived outcomes with those classical integrals. Finally, some nontrivial numerical examples are also provided to verify the correctness of the presented inequalities that occur with the variation of the parameters v and β.

Fuzzy Caputo [formula omitted]-fractional linear equations on the time scale [formula omitted

In this paper, we explore various concepts in q-fractional calculus and extend them to the fuzzy domain. Specifically, we define fuzzy q-exponential and fuzzy q-Mittag-Leffler functions using r-cuts. Additionally, we evaluate the fuzzy Caputo q-derivatives of the fuzzy q-exponential functions and introduce the concept of fuzzy q-Laplace transform. Furthermore, we provide a formula for the fuzzy q-Laplace transform of the fuzzy Caputo q-fractional derivative.We also tackle the problem of solving fuzzy Caputo q-fractional linear differential equations under the generalized Hukuhara differentiability conditions, with a focus on specific forcing functions. The key technique employed for solving these equations is the fuzzy q-Laplace transform. To illustrate the practicality of our theoretical results, we present several numerical examples.This paper contributes to the development of q-fractional calculus in the context of fuzzy mathematics and provides valuable insights into solving fuzzy differential equations using the proposed techniques.