Application Of Fuzzy Logic Research Articles

Enhancing Urban Traffic Flow Through Fuzzy Logic-Based Signal Light Control Optimization

Urban traffic congestion is an urgent problem, which is aggravated by the fixed-time urban traffic signal control system. In order to solve this problem, optimization based on fuzzy logic provides flexibility to adapt to real-time traffic conditions. This study discusses the application of fuzzy logic in traffic control and puts forward the optimization scheme of urban traffic light system, including the design of fuzzy controller, the formulation of fuzzy rules, and the system implementation. The results show that compared with the traditional method, the traffic flow and efficiency have been improved, which shows that it has great potential in the real world. By considering the dynamic characteristics of traffic conditions, this method can realize a more flexible and effective control strategy. It is possible to significantly improve the quality of urban transportation system by implementing this method in real scenes.

Translation of a Bipolar Fuzzy Set in Hilbert Algebras

This study explores the application of bipolar fuzzy set theory within Hilbert algebras, introducing and examining the concept of bipolar fuzzy (β, α)-translations of a bipolar fuzzy set φ =(φ+, φ−) in two distinct forms: Type I and Type II. Fundamental properties of these bipolar fuzzy translations are investigated in depth, alongside the introduction of bipolar fuzzy extensions andintensities, broadening the utility and flexibility of bipolar fuzzy sets in capturing nuanced bipolar information. Moreover, this work addresses the intricate relationships between the complement of a bipolar fuzzy subalgebra, bipolar fuzzy ideal, and bipolar fuzzy deductive system with respect to their level cuts. The findings significantly contribute to the broader theoretical foundation and potential applications of bipolar fuzzy logic in Hilbert algebras, offering valuable insights for managing complex bipolar information in uncertain environments.

Integrating AHP and GTMA for Enhanced Multi-Criteria Decision Making in Logistics Provider Selection

The selection of logistics providers is a critical task for organizations aiming to enhance their supply chain performance and maintain a competitive edge. This paper proposes an integrated approach that combines the Analytic Hierarchy Process (AHP) and Graph-Theoretic Matrix Approach (GTMA) for effective multi-criteria decision-making (MCDM) in the selection of logistics providers. AHP is widely used for evaluating criteria and alternatives based on pairwise comparisons, while GTMA enhances decision-making by incorporating complex interdependencies among criteria. The integrated AHPGTMA approach is demonstrated through a case study involving the selection of a third-party logistics provider (3PLP). The proposed method improves the decision-making process by simplifying complex relationships among criteria while ensuring an efficient and robust selection. This integrated model presents a more comprehensive and reliable framework for logistics provider selection, overcoming limitations observed in stand-alone AHP and ANP approaches. Future research may focus on the application of fuzzy logic to further enhance decision accuracy in uncertain environments.

Critical study of type-2 fuzzy logic control from theory to applications: A state-of-the-art comprehensive survey

The review systematically explores the theoretical foundations of type-2 fuzzy sets and the associated mathematical frameworks of the advancements in type-2 fuzzy logic systems (T2-FLS), as well as a comprehensive overview of the state-of-the-art techniques and applications of type-2 fuzzy logic systems. The review's application-oriented section looks at a number of different areas where type-2 fuzzy logic has made a big difference. These areas include robotics, autonomous vehicle control, electric and electronic control, image processing, and railway control. This overview mainly discusses the past, present, and future trends in type-2 fuzzy logic applications. The primary contribution summarizes the most essential type-2 fuzzy logic research with theoretical and practical implications. This article will provide a critical path and a strong foundation for individuals interested in this field to pursue future research.

Dynamic ranking function to optimize transshipment costs in intuitionistic Type-2 and Type-1 fuzzy environments

In the dynamic realm of organizational logistics, accurately minimizing transportation and transshipment costs is crucial, yet often challenging due to inherent uncertainties. This paper introduces a novel application of fuzzy logic to provide a more precise analysis of these costs. Specifically, it develops an innovative ranking function for trapezoidal fuzzy numbers (TrFNs) for Type-2 and Type-1 fuzzy environments, a tool yet unexplored in existing literature. The main contributions of this paper are the idea that a ranking function for TrFNs can significantly improve decision-maker's freedom in cost analysis due to an adherence on all (a, b, c d) parameters of TrFN. A new decision-oriented ranking method for these fuzzy numbers is developed which consists of an inventive algorithm. The method is also considered for intuitionistic TrFNs and applied to solve transshipment costs in fuzzy area. To verify the proposed methodology's efficiency, effectiveness and accuracy a numerical example in Wolfram Mathematica 9.0 is demonstrated showing superior computational performance over existing methods.

A Fuzzy Logic Application to Manage Construction-Cost Escalation

In large-scale projects, such as transport infrastructures, the cost-planning phase stands out as one of the most crucial for the project’s success. Cost and time overruns, during the construction phase, are often the cause of project failure. A meticulous, conscious, and accurate ex-ante analysis of cost and time assessment can greatly contribute to the efficient and effective completion of a project. Various approaches have been developed to estimate and mitigate cost overruns. Such mitigation is subject to careful analysis of project risks, encompassing construction, environmental, social acceptance, and market risks. In this contribution, we propose the implementation of the risk-assessment tool, suggested by the National Anti-Corruption Authority (ANAC), using fuzzy logic to enhance its effectiveness. Thanks to the implementation of fuzzy prioritizations, the risk matrix and the associated risk levels, which are obtained by combining the event’s probability of occurrence and its expected impact on costs, have been clearly and structurally defined. This tool can facilitate risk ranking and, therefore, the implementation of their management strategies during the design and construction phases as well as the consensus-creation process. This application can therefore be used by public authorities as a transparent and manageable tool to assess expected risk during the design and implementation phases of the project.

Potential Analysis of the Application of Fuzzy Logic in the Optimization of Pak Choi Hydroponic Cultivation: A Literature Study on Plant Growth Support System

The rapid urbanization and increasing population density in Indonesia have led to a reduction in available land for traditional agriculture, particularly in urban centers. In response to this challenge, urban farming, including hydroponic cultivation, has emerged as a viable solution to meet the food needs of urban communities. This study explores the potential application of fuzzy logic in optimizing hydroponic cultivation practices, focusing on the growth of pak choi plants. Pak choi, a popular vegetable in Indonesia, offers numerous health benefits and is well-suited for hydroponic cultivation. By analyzing relevant literature, this research investigates the influence of water pH and temperature on pak choi plant growth and develops a fuzzy expert system based on Mamdani's method. The study prepares data on optimal temperature and pH ranges for pak choi cultivation and establishes fuzzy sets for water temperature, water pH level, and plant height. The findings provide insights into the effects of water pH and temperature on pak choi plant growth and demonstrate the potential of fuzzy logic in optimizing hydroponic cultivation practices. This research contributes to the advancement of agricultural technology and offers practical solutions for sustainable urban farming.

Implementation of Fuzzy Logic to Regulate Water Quality in Maintaining the Aquascape Ecosystem

Aquascape is an artificial aquarium ecosystem that is increasingly popular, considering the high cost of creating and maintaining an aquascape. Several aspects need to be considered in maintaining an aquascape, namely maintaining water quality. To know the water quality, it is necessary to periodically check the aquascape manually and carry out maintenance, therefore knowledge is needed to regulate and maintain the water quality in the aquascape. This research aims to implement fuzzy logic as a water quality control system in aquascape maintenance. The research method includes collecting data from the literature as well as developing a fuzzy logic model to identify and control water quality such as pH and temperature. The output of the system developed is a water quality control device with the categories "Unbalanced", "Quite Balanced", "Balanced". This research method includes calculations by collecting data and using MATLAB software to develop a fuzzy logic system. The research results show that the application of fuzzy logic is able to regulate water quality effectively and minimize the risk of ecosystem damage in the aquascape. The research conclusion shows that the application of fuzzy logic to regulate water quality in aquascapes has great potential in maintaining the sustainability of aquascape ecosystems.

The Application of Fuzzy Logic for the Quality of Salted Egg

The Application of Fuzzy Logic for the Quality of Salted Egg

Applications of Fuzzy Logic and Probabilistic Neural Networks in E-Service for Malware Detection

The key point in the process of agent-based management in e-service for malware detection (according to accuracy criteria) is a decision-making process. To determine the optimal e-service for malware detection, two concepts were investigated: Fuzzy Logic (FL) and Probabilistic Neural Networks (PNN). In this study, three evolutionary variants of fuzzy partitioning, including regular, hierarchical fuzzy partitioning, and k-means, were used to automatically process the design of the fuzzy partition. Also, this study demonstrates the application of a feature selection method to reduce the dimensionality of the data by removing irrelevant features to create fuzzy logic in a dataset. The behaviors of malware are analyzed by fuzzifying relevant features for pattern recognition. The Apriori algorithm was applied to the fuzzified features to find the fuzzy-based rules, and these rules were used for predicting the output of malware detection e-services. Probabilistic neural networks were also used to find the ideal agent-based model for numerous classification problems. The numerical results show that the agent-based management performances trained with the clustering method achieve an accuracy of 100% with the PNN-MCD model. This is followed by the FL model, which classifies on the basis of linguistic variables and achieves an average accuracy of 82%.

Application of Fuzzy Logic for Horizontal Scaling in Kubernetes Environments within the Context of Edge Computing

This paper presents a fuzzy logic-based approach for replica scaling in a Kubernetes environment, focusing on integrating Edge Computing. The proposed FHS (Fuzzy-based Horizontal Scaling) system was compared to the standard Kubernetes scaling mechanism, HPA (Horizontal Pod Autoscaler). The comparison considered resource consumption, the number of replicas used, and adherence to latency Service-Level Agreements (SLAs). The experiments were conducted in an environment simulating Edge Computing infrastructure, with virtual machines used to represent edge nodes and traffic generated via JMeter. The results demonstrate that FHS achieves a reduction in CPU consumption, uses fewer replicas under the same stress conditions, and exhibits more distributed SLA latency violation rates compared to HPA. These results indicate that FHS offers a more efficient and customizable solution for replica scaling in Kubernetes within Edge Computing environments, contributing to both operational efficiency and service quality.

Optimizing full-scale MBR performance: A dual-phase approach for real-time air-scouring and permeate flow modifications

This study reports on an advanced fuzzy logic control system designed to optimize air-scour and permeate flow rates in membrane bioreactors, targeting enhanced energy efficiency and reduced fouling. Developed in two stages, the system integrates real-time monitoring of transmembrane pressure and permeability trends to adjust air-scour rates in response to fouling indicators. The initial simulations indicated potential for a 50 % reduction in air-scour rates, suggesting considerable energy savings. The second stage's refinements, based on empirical validations, yielded a more responsive system, albeit with more conservative operational adjustments for energy savings.Over eight months of full-scale testing, the advanced control system implemented in one of eight filtration lines exhibited lower fouling rates and maintained superior permeability values compared to the other lines. This performance translated into reduced chemical cleaning frequency, thereby affirming the system's efficiency in fouling management. These findings validate the application of fuzzy logic in MBR operations and offer a promising approach for energy-efficient and sustainable wastewater treatment.

Enhancing targeted tumor treatment: A novel fuzzy logic framework for precision drug delivery strategy selection

Objective:This study aims to address the challenge of selecting optimal drug delivery strategies for tumor patients by introducing a novel theoretical framework. Methods:We propose a fuzzy logic-based framework for quantitatively assessing Health States (HS) in tumor patients. This framework integrates quantified HS assessments with causality strength analyses, offering a comprehensive understanding of various drug delivery schemes’ effectiveness from pharmacokinetic and pharmacodynamic perspectives. Results:The efficacy of our approach is demonstrated through a series of real-world patient case studies, highlighting its potential to enhance the evaluation and selection of targeted drug delivery strategies. Conclusion:Our work contributes to the field by showcasing practical applications of fuzzy logic in targeted drug delivery systems (TDDs) and establishing a new benchmark for precision in drug delivery strategy selection. Significance:This study has significant implications for developing personalized medical treatments, potentially revolutionizing the field with a more nuanced and scientifically rigorous method for evaluating and selecting drug delivery protocols. Contributions:Development of a fuzzy logic framework for precise quantification of health states in tumor patients. Innovative integration of a causal system for comprehensively evaluating targeted drug delivery strategies.

Application of Fuzzy Logic to Classify Community Welfare Levels

Information regarding family welfare does not only affect family members, but also influences the success of government, including village government. Therefore, information regarding the level of family welfare is needed to monitor the progress of development programs that have been carried out. The fuzzy logic of the Tahani model is one method that can be applied to classify things. The aim of this research is to classify the level of welfare of families as potential recipients of assistance based on population data held by the Mentawai Social Service & P3A. This research was processed using Fuzzy Tahani logic. Fuzzy Tahani is an optimization algorithm that can be used to support decisions by utilizing relational databases. Based on the research results obtained, fuzzy logic with the Tahani model can be used to process family data in accordance with indicators of family welfare levels by providing output in the form of family classification. It's just that the application of the Tahani model should be done on a single rule search function, not to process all the rules using a Tahani query to produce a family classification

Fuzzy rule–based control of multireservoir operation system for flood and drought mitigation in the Upper Mun River Basin

Strategic reservoir operation, a primary water management measures, plays a significant role in mitigating floods and droughts. Since the reservoir operation involves making complicated decisions on uncertain hydrological variables driven by climate variability, therefore, constructive tool for decision making like fuzzy logic is essential to optimize reservoir management and ensure water security. This study demonstrated fuzzy logic application to multiple reservoir operation in tropical region like Thailand. A Fuzzy Rule–Based Model (FRBM) exploiting FL approach was developed to control the upstream reservoir operation in the Upper Mun River Basin (UMRB) using the data from 2008 to 2021. Implementing FRBM for UMRB was conducted by identifying two key variables; available water storage and 7–day ahead predicted inflow, as fuzzy inputs. The fuzzy output of the system is the release fraction determined by three operational condition modules; flood, neutral, and drought. For flood module, fuzzy release is primarily determined by the predicted inflow. However, the determination of reservoir release for drought and neutral modules is influenced by the targeted water demand. The results of base case illustrate the capability of FRBM in increasing reservoir storages at the start of dry season by 123.56 MCM/yr in UMRB due to the new daily release schemes generated. This allows supplying water closer to the theoretical agricultural needs and gross irrigation water requirement potentially reducing the risk of water shortfall during consecutive dry years. Whereas, the maximum fuzzy release is constrained corresponding to safe channel capacity of tributaries and Upper Mun river, therefore, downstream flooding is accordingly prevented.

Modeling performance evaluation in badminton sports: a fuzzy logic approach

Spectators and many young students have flocked to badminton matches in recent years. Badminton practice has received a lot of media coverage. The current state of badminton evaluation methods is lacking in reliability. This article's overarching goal is to examine the many applications of fuzzy logic in badminton performance evaluation and improvement. Data on the badminton technique's flexion and extension phases are mapped into the suggested model using a fuzzy inference system (FIS). This study suggests a fuzzy logic-based badminton-specific objective fuzzy inference system (Bmt-FIS) to evaluate team sports. Despite the gravity of the situation, decisions involving performance reviews often use subjective data. These common decision-making problems may be realistically addressed by fuzzy logic models. Fuzzy logic has the potential to be an effective tool in situations where both quantitative and qualitative data interpretation are allowed. To do this, it accounts for the inherent variability in athletic performance by taking into consideration the 'hazy' or 'uncertain' limitations of data. By taking limitations into account, a rule-based approach makes performance evaluation more precise. Here, a fuzzy inference system (FIS) uses the input variables to evaluate the student's performance. While data mining approaches have been studied, the adaptive neural fuzzy method outperforms others because of its exceptional accuracy.This method eloquently and clearly conveys the many levels of integrity and ambiguity. Also, fuzzy logic may be a great tool for evaluating badminton skills. This foundational study connects the dynamic realm of sports with static measures.

Intelligent Control of a Laboratory Industrial System (FESTO MPS PA)

The paper investigates the application of genetic algorithms, fuzzy logic, and fuzzy sliding mode to improve control in complex systems, with a specific focus on the FESTO controlled system. Genetic algorithms are utilized to determine the controller parameters, while fuzzy logic and fuzzy sliding mode are employed to handle uncertainty and dynamically adapt control settings. The study delves into the theoretical foundations of these methodologies, outlines the research methodology, and scrutinizes the results. The findings underscore that incorporating these techniques enhances the adaptability and efficiency of the FESTO control system in intricate and uncertain environments. This research lays the groundwork for innovative strategies aimed at enhancing control in complex systems.

Towards Enhanced Autonomous Driving Takeovers: Fuzzy Logic Perspective for Predicting Situational Awareness

This paper investigates the application of fuzzy logic to enhance situational awareness in Advanced Driver Assistance Systems (ADAS). Situational awareness is critical for drivers to respond appropriately to dynamic driving scenarios. As car automation increases, monitoring situational awareness ensures that drivers can effectively take control of the vehicle when needed. Our study explores whether fuzzy logic can accurately assess situational awareness using a set of 14 critical predictors categorized into time decision, criticality, eye-related metrics, and driver experience. We based our work on prior research that used machine learning (ML) models to achieve high accuracy. Our proposed fuzzy logic system aims to match the predictive accuracy of ML models while providing additional benefits in terms of interpretability and robustness. This approach emphasizes a fresh perspective on situational awareness within ADAS, potentially improving safety and efficiency in real-world driving scenarios.

Adjustment of Convolutional and Hidden Layers Using Type-1 Fuzzy Logic Applied to Diabetic Retinopathy Classification

Adjustment of Convolutional and Hidden Layers Using Type-1 Fuzzy Logic Applied to Diabetic Retinopathy Classification

Towards Refined Autism Screening: A Fuzzy Logic Approach with a Focus on Subtle Diagnostic Challenges

This study explores the creation and testing of a Fuzzy Inferencing System for automating preliminary referrals for autism diagnosis, utilizing membership functions aligned with the Autism Quotient 10-item questionnaire. Validated across three distinct datasets, the system demonstrated perfect accuracy in deterministic settings and an overall accuracy of 92.91% in a broad fuzzy dataset. The use of Fuzzy Logic reflects the complex and variable nature of autism diagnosis, suggesting its potential applicability in this field. While the system effectively categorized clear referral and non-referral scenarios, it faced challenges in accurately identifying cases requiring a second opinion. These results indicate the need for further refinement to enhance the efficiency and accuracy of preliminary autism screenings, pointing to future avenues for improving the system’s performance. The motivation behind this study is to address the diagnostic gap for high-functioning adults whose symptoms present in a more neurotypical manner. Many current deep learning approaches for diagnosing autism focus on quantitative datasets like fMRI and facial expressions, often overlooking behavioral traits. However, autism diagnosis still heavily relies on long histories and multi-stakeholder information from parents, teachers, doctors and behavioral experts. This research addresses the challenge of creating an automated system that can handle the nuances and variability inherent in ASD symptoms. The theoretical innovation lies in the novel application of Fuzzy Logic to interpret these subtle diagnostic indicators, providing a more systematic approach compared to traditional methods. By bridging the gap between subjective clinical evaluations and objective computational techniques, this study aims to enhance the preliminary screening process for ASD.