Fuzzy Approach Research Articles

Implementation of Fuzzy Logic Scheme for Assessment of Power Transformer Oil Deterioration Using Imprecise Information

This research aims to analyze the implementation of a fuzzy logic-based approach in improving the diagnosis of power transformer oil deterioration, which is critical for maintaining the efficient performance and operational life of transformers. Traditional diagnoses are based on strict measurements that do not account for the factors of variability and uncertainty of the actual data. In this article, we perform six different types of tests in this regard, and data have been collected during the period of 2021 to 2022 of 188 power transformer failures in the New KotLakhpat Lahore unit, whose voltage range is 132/66 kv and rating capacity is 40/50 MVA. In this case, a fuzzy logic-based scheme is developed based upon the membership function, a rule-based and defuzzification method that works with imprecision and the implementation of uncertainty in assessing the condition of transformer oils. Moisture, acidity, and a dissolved gas analysis indicator, along with other indication approaches such as interfacial tension, viscosity, and tangent delta measurement, are used to analyze the deterioration process in transformer oils. In the visual representation, oil samples with the following properties were first fuzzified: 19.9 mm2/s of viscosity, 0.453 mgKOH/g of acidity, 695 ppm of DGA, 20.8 mg/kg of moisture, 19.98 of IFT, and 4.35 × 100.14 of tangent delta. The output that was generated by software using the values entered into the parameters (HI and Age) after defuzzification is 45. Fuzzy logic serves as a concrete framework for transforming the diagnostics system and deterring the threats to the entire transformer’s health and reliability in the future. By using this technique, various faults were hypothetically and practically analyzed in a transformer to implement early detection technologies with the possibility to reduce maintenance costs and extend operational life up to 45 years. Various case studies indicate the effectiveness of fuzzy logic in comparison to traditional diagnostics.

Clustering based fuzzy classification with a noise cluster in detecting fraud in insurance

Fraud detection is one of the main issues in reducing the unsystematic risks in insurance business as its costs might reach to catastrophic amounts leading to higher loadings on reserves and premiums. Due to its cause of nature in diversity, fraud detection may require a wide range of factors and variables to be considered. To make logical relations between many factors and reveal their differences, estimate odds (or probabilities), and predict the fraud risk, scoring systems become an important aid. In this paper, we introduce a clustering-based fuzzy classification with a noise cluster (CBFCN) to identify the true state of a fraud. The approach proposed in this paper is based on fuzzy k-means clustering having a noise cluster (FKMN) and is a novel method for identifying outliers by achieving robust clustering. We integrate fuzzy theory to boost the prediction ability of machine learning (ML) approaches for a proper determination of the contributing features. The two critical features of the CBFCN method which are the membership values obtained from the FKMN clustering algorithm are implemented to capture the behavior of an existing structure better and detect the noise (extremes) in the dataset. Extensive analyses are made on two real datasets exposing different characteristics in their variables to demonstrate how CBFCN performs in detecting the fraud compared to the conventional approaches. Additionally, employing fuzzy approach to improve the ML performance is elaborated through the inclusion of noise clusters. The findings indicate that the suggested CBFCN models produce promising classification results in fraud detection in insurance claims occurrences.

Sustainability Service Chain Capabilities in the Oil and Gas Industry: A Fuzzy Hybrid Approach SWARA-MABAC

This study aims to redefine the capabilities of the service supply chain in the Iranian oil and gas industry, where the concept of service chains has remained underdeveloped due to a traditional focus on conventional service chains. It seeks to compare the capabilities of the oil and gas service supply chain to those in other sectors and explore whether applied research is necessary to improve service chain performance. The study was conducted in three stages, involving ten petroleum and natural gas experts. The first stage focused on identifying key capabilities, yielding seven significant capabilities and 28 critical sub-capabilities. In the second stage, the SWARA fuzzy hybrid approach was employed to weigh and prioritize these capabilities, while the fuzzy MABAC methodology was used for strategic decision-making. Finally, the model’s sensitivity was assessed using fuzzy methods to validate the findings. The results highlight the highest priority capability for service providers: the ability to utilize information for updating information processing capacity in making decisions within the service supply chain. The selected location, G1, emerged as a key area of focus. This approach presents a novel method for optimizing service supply chain locations within the oil and gas sector. This paper introduces a unique approach to selecting optimal service supply chain locations in the oil and gas industry, addressing critical gaps in previous research. Employing advanced fuzzy hybrid methodologies provides valuable insights into improving service chain capabilities, offering a competitive advantage for companies operating in this sector.

Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease

Dengue fever is a serious public health issue worldwide, particularly in tropical and subtropical areas. Early detection and accurate diagnosis are essential for effective management and control of the disease. In this study, we present a fuzzy hybrid approach (F-TLBO-APSO) for the detection and diagnosis of dengue disease using an advanced teaching-learning technique with adaptive particle swarm optimization. The proposed method combines the strengths of fuzzy logic, teaching learning-based optimization (TLBO), and adaptive particle swarm optimization (APSO) to improve the accuracy and efficiency of dengue detection based on symptoms. A key challenge addressed is the management of uncertain information existing in the problem. To validate the proposed technique, we applied it to a case study, demonstrating its robustness. The results indicate the versatility of the F-TLBO-APSO algorithm and highlight its value in detecting dengue based on symptoms. Our numerical computations reveal the advantages of the F-TLBO-APSO algorithm compared to TLBO and APSO.

FUZZY LOGIC-BASED POWER MANAGEMENT IN GRID-CONNECTED SOLAR ENERGY SYSTEMS

With the ever-increasing application of photovoltaic systems on the power grid, energy flow management and grid stability have become a key factor, especially under varying conditions like variable cloud cover and fluctuating load demands. The paper looks forward to proposing a fuzzy logic approach to the management of energy regarding a solar energy system connected to a grid, aiming at obtaining an optimised power distribution among PV generation, the grid, and battery storage. The proposed system will be driven by five input parameters, PV value, grid availability, load demand, battery state of charge, and tariff rate, to regulate the energy flow activity within different scenarios. The simulation results developed in the MATLAB Simulink environment proved the effectiveness of the applied controller in maintaining energy stability and fulfilling the requirements of a load within seven case studies for a 24-hour duration. The work points out the potential of fuzzy logic, besides reliability, in enhancing the efficiency of various renewable energy systems integrated with conventional energy supply grids.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0799/a.php" alt="Hit counter" /></p>

Robustness assessment of RC structures designed for progressive collapse under multiple hazards

The goal of robustness assessment is to enable stakeholders to obtain efficient information, for reducing potential adverse effects of hazards before a natural disaster occurs, to obtain more realistic evaluation results and retrofit alternatives. A series of tests were carried out to investigate the effect of secondary beam on resisting progressive collapse. To further estimate the structural performance, a risk-based fuzzy conditional robustness assessment approach is established. The developed framework is applied to three various design RC structures subjected to multiple hazards, which is simulated by OpenSEEsPy. In this framework, the effect of the dependence configuration and the epistemic uncertainty on the vulnerability curves, loss-PGA curves, and the robustness index are studied by introducing the Vine-Copula and fuzzy theories. Results show that the effect of epistemic uncertainty on the hazard consequence becomes more pronounced with the increasing of PGA, and the lack of such consideration causes incurring additional retrofit costs for the stakeholders. Moreover, the fuzzy conditional robustness index of the models indicated that the installation of secondary beam showed a superior performance, compared with another reinforcement strategy of additional steel ratio. Finally, a method of survival probability-based robustness assessment is built on the developed assessment procedure and verified.

Enabling sustainable circular economy in Indian municipal solid waste management system: an ISM and fuzzy MICMAC approach

PurposeMismanagement of municipal solid waste (MSW) has led to resource depletion, ecosystem degradation and socio-economic risks, emphasizing the necessity to promote sustainable circular economy (SuCE) practices. This article identifies relevant enablers and unveils their interconnected roles in facilitating a municipal solid waste management (MSWM) system aligned with the principles of SuCE.Design/methodology/approachTo identify pertinent enablers for MSWM systems based on SuCE principles, the study conducted a literature review and engaged experts from waste management sectors and academia to consolidate the final list of enablers. Following this, the study investigated the inter-relationships among these enablers using both Interpretive Structural Modeling (ISM) integrated and fuzzy Matrix Impact Cross-Reference Multiplication Applied to a Classification (fuzzy MICMAC) with MATLAB.FindingsThe study identified a comprehensive set of 23 enablers and categorized them into four control measures instrumental for the effective integration of SuCE principles in the context of the Indian MSWM system. Notably, the analysis highlighted that “authoritative pressure” and “policy transformation” are the highly influential enablers.Practical implicationsThe study emphasizes actionable implications for stakeholders in the Indian MSWM sector. Policymakers and governmental bodies can enforce “authoritative pressure,” “policy transformation” and “budget allocation”. Meanwhile, managers can focus on “promotion of SuCE awareness”, “formalizing the informal workforce”, “infrastructure modernization”, “stakeholder engagement and alignment”, “segregation strategy”, “disruption preparedness”, and “local-based strategy”.Originality/valueThis study proposed a unique ISM diagraph and cluster of enablers to enhance the novelty of the study.

A Fuzzy Logic Approach to Performance Evaluation of Genomics-Driven Crop Improvement

Genomics-driven crop improvement has emerged as a revolutionary approach to enhance yield, resilience, and nutritional value in agriculture. However, traditional performance evaluation methods often must catch up with agricultural systems' complex, multifaceted nature. This study proposes a fuzzy logic-based framework to assess the performance of genomics-driven crop improvement initiatives. By integrating fuzzy logic, we can effectively manage uncertainty and ambiguity inherent in agricultural data, allowing for a more nuanced evaluation of crop traits, environmental factors, and management practices. The paper begins by elucidating the fundamental principles of genomics-based agriculture and its impact on crop development. It then delves into the theoretical underpinnings of fuzzy logic and its applicability in modeling the multifaceted aspects of crop growth, encompassing genetic, environmental, and physiological factors. Through a synthesis of existing research and empirical data, the paper illustrates the effectiveness of fuzzy logic in capturing the nuances of crop development processes, including germination, flowering, and yield formation. This paper underscores the pivotal role of fuzzy logic analysis in advancing genomics-based agriculture, offering insights into the dynamic interactions shaping crop development and facilitating informed decision-making for sustainable and resilient food production systems in the face of evolving environmental challenges. Results indicate that the fuzzy logic approach enhances the accuracy of performance assessments and facilitates better decision-making in crop management.

Teacher Perceptions of Deep Learning Models for Special Need Students in Inclusive Vocational Schools: A Fuzzy Logic Analysis

This study investigated teacher perceptions of the Deep Learning model for students with special needs in a vocational school practicing inclusive education. Utilizing the fuzzy logic approach, data from teacher observations and surveys were processed. The research emphasized the critical role of Deep Learning model in addressing the challenges in inclusive education. Teacher insights are essential for enhancing the efficacy of the model in catering to the unique learning needs of special students. Understanding their perspectives contributes to the ongoing discourse on inclusive education, offering valuable insights for educators and policymakers striving to create more inclusive and equitable learning environments. This research contributes to the ongoing dialogue on inclusive education by shedding light on the practical implementation of the Deep Learning model for students with special needs in vocational education settings.

A Configural Theory of Person-Environment Fit Through the Lens of Collectivism

ABSTRACT Fit with multiple layers of the organizational environment is essential for the effective functioning of employees. However, our knowledge of complex co-influences of multiple person-environment (p-E) fit types on work outcomes remains mostly theoretical. Drawing on sensemaking theory and collectivistic cultural frameworks, this study develops a configural theory of p-E fit. We then apply a fuzzy set approach to empirically demonstrate that work attitudes (job satisfaction, organizational commitment, and turnover intention) are a function of (a) employees’ holistic perceptions of p-E fit instead of independent fit dimensions; and (b) configurations consisting of multi-fit perceptions are formed in response to a confluence of both in-group and institutional collectivism. We also show that p-E fit perceptions are characterized by equifinality (i.e. multiple solutions exist for each work attitude). Our study not only applies a cultural lens to examine multi-fit models but also highlights the importance of fine-tuning complex collectivistic influences.

Application of intuitionistic fuzzy set in social life cycle impact assessment

Abstract Purpose Intuitionistic fuzzy set (IFS) has been found promising for handling ambiguity forms of uncertainty in social data. This paper undertakes an application of an intuitionistic fuzzy set in the social life cycle impact assessment (S-LCIA) of a public building project. This work proposes to combine an intuitionistic fuzzy set (IFS) with multi-attribute decision-making in converting qualitative data into quantitative social impacts of the building project. This research utilises IFS to accommodate and handle experts’ imprecise cognitions in S-LCIA to facilitate the identification and prioritisation of the most pressing indicators accompanying social impacts in a project. Methods Data were collected using questionnaire(s), structured interviews, and through targeted focus group sessions. Intuitionistic fuzzy set (IFS) approach was used to model the obtained data from structured interviews. Using IFS, this research also accounts for missing or ambiguous data that emerged during the data elicitation process. In combining IFS with multi-attribute decision-making techniques, the social impacts of selected stakeholders were evaluated using fuzzy set and IFS approaches. Sensitivity analysis was then used to test the robustness of the results and ranking was conducted based on each social stakeholder subcategory. Results and discussion Results revealed that approximately 23% of missing datapoints in the public case study building were incorporated in the S-LCIA using IFS. The issues of highest priority in each of the considered subcategory in the public case study building project according to IFS are (i) consumer privacy by adopting more functional planning (0.27), (ii) public commitment to sustainability (0.33), and (iii) education provided in the local community by having an indigenous botanical element for children (0.39), respectively. Conversely, the FS technique inferred that the issue of highest priority in the consumer stakeholder is the feedback mechanism (0.26). The overall degree of correlation between the IFS and FS is only 0.234, revealing that IFS provides a different perspective from conventional FS when used in modelling social data. Conclusion The IFS method provides an objective and systematic approach for dealing with a heterogeneous scope of imprecise and inexact social data in achieving holistic social life cycle assessment results. Furthermore, expansive stakeholder involvement would rely on a robust approach for improving social well-being in public buildings, thereby leaving no one behind in accomplishing a sustainable world.

New delay‐dependent finite‐time stabilization of Takagi–Sugeno fuzzy approaches for delayed nonlinear systems

AbstractIn this paper, the finite‐time stability and stabilization of nonlinear systems with delays is studied, via a Takagi–Sugeno approach. By using a novel Lyapunov–Krasovskii functional and introducing some fuzzy free‐weighting matrices, sufficient conditions are derived, for bounded and differentiable time‐varying delays in terms of an upper bound of the delay derivatives. Then, we achieve closed‐loop stabilization in finite time through an efficient parallel distributed compensation design. The sufficient conditions are formulated as linear matrix inequalities to achieve the desired performance. Finally, the proposed methodology is applied to various case studies, highlighting its significance.

Decoding organisational attractiveness: a fuzzy multi-criteria decision-making approach

Decoding organisational attractiveness: a fuzzy multi-criteria decision-making approach

A framework to assess the effectiveness index (EI) of the Indian SMEs to integrate lean and sustainability through combined fuzzy logic and systems approach

PurposeTo become a sustainable lean manufacturing (LM) system, an organization must be first distinctly cognizant of the terms “lean” and “sustainability” as they relate to the state of affairs of their particular industry and business. Next, the organization must identify and acquire the necessary qualities it needs to become sustainable in lean philosophy and its practices in the organization. The LM paradigm has been a top priority for many businesses; thus, this article is based on actual research done in Indian small and medium scale organizations to see how widely it is understood and implemented.Design/methodology/approachA framework was developed based on literature review and academic research. A preliminary analysis of a small number of small and medium-sized enterprises (SMEs) that, conceptually, summarizes and demonstrates the concerted efforts that a company may undertake to increase its leanness. This conceptual model was employed to create a questionnaire that was administered to survey the SMEs of India. The information gathered through this questionnaire was analyzed using the model developed by the researchers. Then fuzzy logic and systems approach were used to find out the effectiveness index (EI) of the organization.FindingsThe EI for system leanness at different organizational levels within an organization is determined using fuzzy logic and systems approach for 48 SMEs in different clusters. The average EI of the system was found to be 0.336 on a scale of 0–1 which indicates that the current state of lean implementation and its sustainability is very low and poor in Indian SMEs. This article outlines the key model components and describes how they were applied to analyze the data gathered from an industry study.Research limitations/implicationsThe research aims to make lean continuously sustainable by surfacing and eliminating the wastes in the Indian SMEs whenever it appears rather than using it as a cleaning tool. The present study was focused on India’s southern industrial areas and it was difficult to gather the information around the country due to its diverse industrial culture and geography. Hence, more research and the comparative study of the same that takes into account the various regions of the nation’s industrial lean behavior can be conducted.Practical implicationsThe generalized sustainable lean framework analyzed using fuzzy logic and systems approach gives the current effectiveness of the leanness in SMEs of south India. This model can be effectively implemented in other areas of the nation to identify the scenario of lean and its sustainability and a final comprehensive model can be developed.Originality/valueThere is a dearth of comprehensive studies on the assessment of sustainability of the lean philosophy in Indian SMEs. With the help of combined fuzzy logic and systems approach, the model developed in this study evaluates the sustainability of the lean methodology using the EI used in SMEs by taking into account both the lean and sustainability factors as well as enablers like customer satisfaction, ethics, innovation and technology.

Analysis of Second-Order Linear Fuzzy Differential Equation Under an Innovative Fuzzy Derivative Approach and Its Application

Fuzzy derivatives are a concept based on fuzzy calculus, which extends classical calculus to handle uncertainty and vagueness, habitually represented using fuzzy sets or fuzzy numbers. In fuzzy calculus, the derivative incorporates fuzziness, meaning both the input and output can have imprecise or uncertain values. In this paper, the step size is also considered as a fuzzy number. The impact of fuzziness in step size is studied here for first- and second-order fuzzy derivatives. Under this proposed derivative, differentiation of fuzzy exponential function is estimated. The concept is applied for solving second-order linear fuzzy differential equation. The proposed approaches are applied to spring mass system dynamics in fuzzy environment. Lastly, numerical illustrations on second-order fuzzy differential equations are shown using analytical technique and graphically the results are displayed.

Optimizing vehicle edge computing task offloading at intersections: a fuzzy decision-making approach

Optimizing vehicle edge computing task offloading at intersections: a fuzzy decision-making approach

Integrated and sustainable performance evaluation of urban rail transit systems using fuzzy sustainability index

The complexity of prominent dimensions in urban rail transportation sector arises from the involvement of several stakeholders and the various interacting variables for evaluating transportation performance. This study uses a fuzzy logic-based method to present an index-based framework for sustainable performance assessment. The proposed framework presents 4 main indicators which include environmental, economic, financial and social sustainability, 14 sub-indicators, 4 main indicators for transportation system effectiveness with 13 sub-indicators and 1 main indicator for multi modal integration with 6 sub-indicators. The performance ratings and importance weights of the transport sustainability attributes are evaluated using a fuzzy logic approach. A numerical illustration is provided to demonstrate the applicability of the approach. The results show that system capacity (passenger capacity of a route), comfort and customer service (passenger environment and ride quality, maintenance cycle) and multimodal integration (park and ride facilities) were found to be weaker attributes. The proposed methodology facilitates the systematic understanding of the prominent attributes for achieving integrated and sustainable transport performance by using the ‘Fuzzy Transport Sustainability Index’.

A Fuzzy Multi-Criteria Approach for Selecting Sustainable Power Systems Simulation Software in Undergraduate Education

A Fuzzy Multi-Criteria Approach for Selecting Sustainable Power Systems Simulation Software in Undergraduate Education

Approximating technology innovation performance with fuzzy approach

Purpose This study aims to construct a framework to evaluate technology innovation performance (TIP) of manufacturing organizations by adopting a fuzzy-based approach. In very short time, the world’s economic order has been reformed by economic globalization, thereby bringing new challenges as well as opportunities for the manufacturing industries. Policy makers encounter different decisions that require the use of various types of data in their decision-making process. These challenges raised the necessity of measuring innovation capability, which is critical issue for decision makers in today’s competitive world. The research results reveal the complexity of the path to technology innovation evaluation, constituting a novel contribution to the literature. Design/methodology/approach It is difficult for decision makers to make appropriate and effective decisions without knowing the innovation capability of companies in a particular sector or a region. As a result, not only on a macro but also on a micro level, an integrated and complete technique of measuring, estimating and even projecting innovation performance is necessary. In light of above mentioned facts, the present study proposes a technology innovation performance evaluation system of manufacturing organizations using fuzzy logic. Findings A model has been developed to be beneficial for any kind of organization where TIP evaluation is important consideration for enhancing manufacturing performance. Fuzzy control is used to determine the overall performance index by combining results of the TIP in selected criteria, which will certainly ensure suitability of the concerned organizations during performance rating calculations. Originality/value This study elaborates a fuzzy model to predict TIP with fuzzy rules. This is the first time a TIP evaluation model is developed using fuzzy approach that will be applicable for any kind of manufacturing industry where TIP evaluation is considered to be significantly important for cooperation’s to sustain competitiveness in the dynamic landscape.

The Natural Pattern-based Fuzzy Investment System

Research background: The underlying research problem concerns simplifying nature’s infinite sustainability pattern into a usable system to prove self-care in the financial field. Many disciplines have discovered self-sustaining systems, implying one should also exist in the portfolio- or asset management field. Purpose of the article: This paper models and applies a self-sustaining investment system based on nature’s sustainability pattern to present a usable and easily adaptable financial self-care model that can contribute to societal and individual well-being. Methods: This interdisciplinary research incorporates fractal patterns from nature and integrates these into an innovative investment decision system via a working and utilizable model. The article approaches the problem in three ways. First, it applies an unprecedented, proprietary fuzzy heuristic approach to parse the Mandelbrot set hiding nature’s growth code. Second, it subjects the resulting model system to static, dynamic, and iterative methods. Finally, it tests the above in practice in a focus group research project based on individual decisions in a portfolio collision. Findings & value added: The paper brings manageable order to investment decision processes using a specific econophysics approach to provide a complex whole of frames, alternatives, and dynamic wealth management functions. The paper attempts to demonstrate the self-sustaining power of the natural order in investment portfolio returns, behavioral finance, and wealth management decisions. The theory of an efficient, well-functioning, self-sustaining investment decision system based on heuristic criteria is developable and has been proven. The study made the organizational dominance measurable and – through fuzzy logic – simplified the complexity of the Mandelbrot set. The research also shows the scalability of the fuzzy symmetry framework, implying that it is transferable to other disciplines.