Application Of Fuzzy Logic Research Articles

Application of Fuzzy Logic in Air Conditioner Temperature Control in Rooms with Partitions in Boarding Houses

This research investigates the application of fuzzy logic with the Mamdani method to regulate room temperature using an air conditioner (AC) in a boarding house environment with a partition at Bogor City, Indonesia. By considering variables such as room size, external temperature (weather), and air conditioner specifications, this research aims to achieve a balance between occupant comfort and energy efficiency. The optimal temperature for each room in the boarding house will be determined through fuzzy logic calculations. This research aims to open up new potentials in intelligent and sustainable temperature regulation in a boarding house or similar environment. This research method uses fuzzy logic for room temperature control using Matlab software.

AI-Based Supply Chain Risk Management

As the complexity of global supply chains increases, supply chain risk management becomes increasingly important. The development of artificial intelligence (AI) technology provides new tools and methods for managing supply chain risks. This paper aims to review the application of AI in supply chain risk management, exploring current research progress, challenges, and future development trends. Through a systematic literature search and analysis, this paper examines relevant papers from databases such as Google Scholar, Web of Science, EI, and Scopus, discussing the specific applications of machine learning, deep learning, neural networks, fuzzy logic, genetic algorithms, and evolutionary algorithms in supply chain risk management. The study finds that these AI technologies demonstrate significant advantages in risk prediction, anomaly detection, image recognition, text mining, supply chain optimization, and emergency strategy formulation. However, issues related to data privacy and security, technical complexity, and implementation difficulties remain major challenges in current applications. In the future, as AI technology advances and interdisciplinary integration develops, supply chain risk management will have more opportunities. This study provides specific application suggestions for enterprises and decision-makers and points the way for future research.

Prediction of seam strength of cotton canvas fabric using fuzzy logic

This study investigates the application of fuzzy logic in predicting seam strength in cotton plain canvas fabric, focusing on both warp and weft directions. The precise prediction of seam strength is crucial for manufacturers to uphold quality standards, enhance production efficiency, and minimize waste. The fuzzy logic model in this study uses thread linear density and stitch per inch as input parameters and warp and weft seam strength as output variables. The modeling was conducted using MATLAB, specifically utilizing the Mamdani fuzzy inference system with triangle membership functions. The fuzzy logic model was found to be very accurate, as shown by coefficients of determination (R2) of 0.9841 for the warp way and 0.9888 for the weft way, along with correlation coefficients (R) of 0.992 and 0.9944. The mean absolute percentage error (MAPE) was calculated to be 4.8719 % for the warp way and 4.7561 % for the weft way, each below 5 %, underscoring the model's reliability and robustness in seam strength prediction. This research provides findings with substantial implications for the textile industry, where the application of predictive models is on the rise to enhance production efficiency and product quality. Manufacturers can improve their ability to forecast regarding fabric properties and adjust production processes through the implementation of fuzzy logic models. This approach is consistent with current industry trends emphasizing automation and digitalization, wherein predictive models are essential for facilitating smart manufacturing and quality control.

Breaking the digitalization barrier for SMEs: a fuzzy logic approach to overcoming challenges in business transformation

The purpose of this study was to investigate the barriers and drivers of digitalization in small- and medium-sized enterprises (SMEs). Specifically, the study aimed to identify the key factors influencing the adoption and implementation of digital technologies in SMEs, understand the challenges faced by these organizations in their digital transformation journey. In this study, a fuzzy logic methodology was employed to assess the digitalization process of 4531 SMEs. Additionally, an analytical hierarchy process (AHP) was applied to determine the weights of the dimensions in the fuzzy analysis. This allowed for a more accurate and precise assessment of the digitalization levels in SMEs. Furthermore, statistical analysis techniques were utilized to analyze the data and draw meaningful conclusions. The study revealed that insufficient financial resources, high investment costs, resistance from workers, a shortage of well-qualified staff, lack of knowledge about technology providers, high IT security requirements, the absence of a digital transformation-oriented business culture, and the application of fuzzy logic as a methodology were the key findings in the digitalization process of SMEs. These findings highlight the complex challenges faced by SMEs and emphasize the need to address these barriers to facilitate successful digital transformation.

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.