Fuzzy Logic Model Research Articles

Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture

AbstractAquaculture plays a vital role in global food production, and maintaining optimal water quality is essential for the health and growth of aquatic species. This research addresses the need for an efficient, adaptive solution by introducing an innovative water quality monitoring and maintenance system for aquaculture ponds. Unlike conventional systems, our approach uniquely integrates fuzzy logic with IoT technologies to optimise the precision and adaptability of pond management. The system stands out with its continuous monitoring of critical parameters such as temperature, pH, dissolved oxygen (DO), weather conditions and salinity and its ability to autonomously adjust operational controls, such as aerators and water pumps, based on dynamic environmental changes. This ensures ideal water conditions without manual intervention, providing a reliable and effective solution for aquaculture pond management. This work’s novelty lies in applying fuzzy Logic to handle the complexity and variability of aquaculture environments, allowing for nuanced control decisions that improve water quality management. The system’s efficiency was demonstrated through a 72-h operational test, where it maintained optimal DO and salinity levels, showcasing its reliability and effectiveness in real-world conditions. The fuzzy logic model has demonstrated a commendable accuracy rate of 98%. These results validate the system’s performance and underscore its practical benefits, meeting the demands of aquaculture production and significantly enhancing operational efficiency by enabling remote monitoring and rapid issue identification. This research contributes a robust technological solution for aquafarmers, offering a promising advancement in aquaculture management by improving productivity and ensuring the health and growth of aquatic species.

Development of the intelligent traffic light system based on image processing and fuzzy control techniques

In Viet Nam's current traffic conditions, congestion and jams—especially at intersections during peak hours—present major challenges. Traditional traffic light systems, which rely on fixed timing principles, often fail to manage traffic flow efficiently, particularly when vehicle density varies significantly across different directions. This research aims to develop an intelligent traffic light system where the signal timings automatically adjust based on the vehicle density at intersections. The study uses an object recognition algorithm to identify, classify, and count vehicles. The data was then fed into a fuzzy logic model to calculate the optimal signal timings. Experimental results demonstrate an accuracy of approximately 88% in vehicle detection. The fuzzy logic model and the programmable logic controller were able to effectively compute reasonable signal timings based on real-time vehicle density. Future developments include expanding the system's functionalities, creating a user-friendly interface, and developing a management application for mobile devices.

Fuzzy Logic Concepts, Developments and Implementation

Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic algorithms, creating powerful tools for complex problem-solving applications. This article provides an informative description of some of the main concepts in the field of fuzzy logic. These include the types and roles of membership functions, fuzzy inference system (FIS), adaptive neuro-fuzzy inference system and fuzzy c-means clustering. The processes of fuzzification, defuzzification, implication, and determining fuzzy rules’ firing strengths are described. The article outlines some recent developments in the field of fuzzy logic, including its applications for decision support, industrial processes and control, data and telecommunication, and image and signal processing. Approaches to implementing fuzzy logic models are explained and, as an illustration, Matlab (version R2024b) is used to demonstrate implementation of a FIS. The prospects for future fuzzy logic developments are explored and example applications of hybrid fuzzy logic systems are provided. There remain extensive opportunities in further developing fuzzy logic-based techniques, including their further integration with various machine learning algorithms, and their adaptation into consumer products and industrial processes.

Compressive strength of nano concrete materials under elevated temperatures using machine learning

In this study, four Artificial intelligence (AI) - based machine learning models were developed to estimate the Residual compressive strength (RCS) value of concrete supported with nano additives of Nanocarbon tubes (NCTs) and Nano alumina (NAl), after exposure to elevated temperatures ranging from 200 to 800 degrees. These models were developed via adapting meta- heuristic models including the Water cycle algorithm (WCA), Genetic algorithm (GA), and classical AI models of Artificial neural networks (ANNs), Fuzzy logic models (FLM), in addition to the statistical method of Multiple linear regression (MLR). 156 post heating experimental results available as a literature data (represents four input parameters of temperature change, heat exposure duration, nanomaterial type, and replacement proportion) are used to achieve the study’s objective. Results of the developed models demonstrated that ANN and FLM have strong potential in predicting RCS. However, it is often infeasible to generate practical equations that relate input and output variables from these models. Upon analysing the results of the WCA and GA, it was found that WCA yielded the most accurate predictions based on all performance indicators. Furthermore, RCS prediction equations with superior accuracy were derived utilizing the meta-heuristic AI models of WCA and GA, with Mean absolute errors (MAEs) of 3.09 kg/cm² and 3.53 kg/cm² for the training, 1.91 kg/cm² and 2.72 kg/cm² for the validation, and 1.91 kg/cm² and 2.72 kg/cm² for the testing data sets, respectively. Additionally, sensitivity analysis via neural networks weights and SHAP investigation were performed to reveals the impact and relationship of the input variables with the output variables. Both techniques reveal that temperature degree and time of exposure had the highest positive impact on RCS value, followed by NAl and NCTs, in order.

Fuzzy Logic-Based Landslide Susceptibility Mapping in Earthquake-Prone Areas: A Case Study of the Mila Basin, Algeria

Abstract —This research focuses on analyzing landslides triggered by a moderate earthquake (Mw = 4.9) in the northeastern region of the Mila province, which resulted in significant damage and economic losses in the El Kherba district and Grarem Gouga city. Through an extensive field-based investigation, a comprehensive inventory of landslides has been compiled. To assess the susceptibility to landslides triggered by seismic activity, a GIS-based fuzzy logic model was employed. The model incorporates various input factors, such as lithology, slope angle, normalized difference vegetation index (NDVI), distance from rivers and roads, precipitation, and seismic hazard, which is shown on a map. The study compares the performance of different fuzzy operators and gamma values and determines that using fuzzy gamma operators with a gamma value of 0.8 yields a satisfactory consistency with the distribution of landslides. Moreover, incorporating the map of seismic hazard as a causative factor enhances the accuracy of landslide susceptibility mapping. This study underscores the utility of the fuzzy logic model in disaster management and the planning of development activities.

Long term demand forecasting in Jakarta distribution grid system using fuzzy logic and artificial neural network method

Abstract In order to attain equilibrium between energy supply and demand, reliance on conventional methods for precise long-term electricity demand forecasting is no longer viable. The utilization of artificial intelligence, such as fuzzy logic and artificial neural network (ANN) models, emerges as a prospective solution in the current dynamic scenario. This research explores long-term electricity demand forecasting within the Jakarta distribution grid system, employing various fuzzy logic and ANN approaches including Sugeno, Mamdani, Bayesian Regularization, and the Levenberg algorithm. The analysis incorporates time series data spanning 2016 to 2019, encompassing electricity load demand, economic factors, and demographic variables, processed using MATLAB. The outcomes of the four forecasting methods reveal an average error range of 2 to 3%. The findings indicate that employing fuzzy logic and ANN methods for long-term electricity demand forecasting can yield a forecast error of less than 3%. The study recommends future research enhancements through the inclusion of additional time series data and a more refined system.

Fuzzy Logic Model for Selection of Performance Upgrade Alternatives in Remanufacturing: Case of Brake Caliper Component

Fuzzy Logic Model for Selection of Performance Upgrade Alternatives in Remanufacturing: Case of Brake Caliper Component

Modification of the microclimate and fire danger by Berg winds in the midlands of KwaZulu-Natal

ABSTRACT Wildfires have the potential to result in significant loss of life and property damage. Mountain wave flow events (Berg winds) are integrally linked to periods of enhanced fire danger. The basis for enhanced fire danger during periods of mountain winds has long been established, supported by evidence from a number of case studies. A fuzzy logic model, applied to hourly meteorological data from four different sites varying in elevation, allowed for an in-depth study of the effects of Berg winds on the microclimate and fire danger. Results indicate that night-time Berg wind events occur less frequently than daytime events, also having a significantly reduced influence on the microclimate. Daytime Berg wind events were shown to result in a significant change in the microclimate and fire danger across all sites. Night-time Berg wind events did not result in significant change to air temperature and relative humidity but when combined with significant changes to wind speed did result in significant increases in fire danger. The sensitivity of the Lowveld Fire Danger Index was also tested. The study emphasizes the need for boundary layer processes such as Berg winds to be taken into account when forecasting and monitoring fire danger.

Response Coordination Model for Real Time Decision Making in Coastal Search and Rescue Operation Using Fuzzy Logic Technique

Coastal search and rescue (SAR) operations are complex, involving dynamic and uncertain conditions that demand real-time, effective decision-making. This paper aimed to analyze response coordination for real- time decision-making in coastal SAR operations using the fuzzy logic technique. The main aims of this study were to identify the key parameters and linguistic variables critical for effective decision-making in SAR operations and finally to design a fuzzy logic model tailored to the dynamic and uncertain conditions inherent in coastal SAR operations. The proposed fuzzy logic model demonstrated improved responsiveness and adaptability to changing conditions, offering a more robust framework for decision-making in SAR operations. However, this study contributes to enhancing the efficiency and effectiveness of real time decision making in SAR operations in coastal environments, with broader implications for maritime safety.

Machining Data Selection by Using a Fuzzy Logic Model

The fuzzy model has been developed for machining parameter selection. The correlation between the hardness of the work-piece and cutting velocity is not exact, but a proper relationship can be established by using a developed model. The fuzzy model was applied to machining data for carbon steel (wrought) that are collected from the ASM handbook, and a proper relationship between handbook data and predicted data by the fuzzy application was obtained. The model was developed by considering input hardness and output speed. A linear fuzzy equation has been proposed to represent the machining data with good correlation. The aim of the present work is to develop a fuzzy logic model to enable the computerization process of machining data that is available in the machining data handbook. As a result, a fuzzy logic-based expert system for machining data selection can be created.

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.

Factors Affecting Control Room Operator Situation Awareness and ITS Fuzzy Logic Model

Factors Affecting Control Room Operator Situation Awareness and ITS Fuzzy Logic Model

Age-friendly human resource practices: a comparison of Baltic and Nordic countries

PurposeDue to population ageing, the European Union (EU) has adopted active ageing as a guiding principle in labour and retirement policies. Among the strategies for active ageing, age-friendly workplaces play a crucial role. This study compares age-friendly human resource (HR) practices in the Baltic and Nordic countries. The latter are pioneers in active ageing, and as the employment rate of older employees in the Baltics is like that in the Nordic countries, we may assume equally age-friendly workplaces in both regions.Design/methodology/approachWe used the latest CRANET survey data (2021–2022) from 1,452 large firms in seven countries and constructed the fuzzy logic model on age-friendliness at the workplace.FindingsDespite a high employment rate of older individuals in the Baltics, HR practices in these countries fall short of being age-friendly compared to their Nordic counterparts. Larger firms in the Nordic countries excel in every studied aspect, but deficiencies in the Baltics are primarily attributed to the absence of employer-provided health and pension schemes. The usage of early retirement is more frequent in the Nordic countries; however, its conceptualisation as an age-friendly HR practice deserves closer examination. Our findings suggest that the success of active ageing in employment has translated into age-friendly HR practices in larger organisations in the Nordics, but not in the Baltics. It is likely that high employment of older individuals in Estonia, Latvia and Lithuania is a result of the relative income poverty rate.Originality/valueOur model represents one of the few attempts to utilise fuzzy logic methodology for studying human resource practices and their quantitative evaluation, especially concerning age-friendly workplaces.

Predicting Heat Transfer Enhancement with Twisted Tape Inserts Using Fuzzy Logic Techniques in Heat Exchangers

Fuzzy logic, introduced by Lotfi Zadeh in 1965, is a powerful method for modelling complex experiments. This study utilizes fuzzy logic to simulate and predict heat transfer in a double-pipe heat exchanger equipped with wavy inserts. The inserts, in the form of twisted tapes, have varying twist ratios (TR=9, 7, 6). The study investigates a range of Reynolds numbers (Re) from 6000 to 18000, with friction factors ranging from 0.03620 to 0.08231, and Nusselt numbers (Nu) between 66.13 and 253.28. The results for different twist ratios are compared to the ideal case. The experimental results indicate that the highest heat transfer occurs with a twist ratio of 6, leading to a significant increase of 162% in the Nusselt number and a 36.21% rise in the friction factor compared to the ideal scenario. In the fuzzy logic framework, the input variables are the twist ratio (Tr), temperature, and Reynolds number (Re), while the output variables are the friction factor (f) and Nusselt number (Nu). The study demonstrates that the Mamdani fuzzy inference system is an exceptionally effective tool for predicting experimental outcomes, given its low error rate. Upon analysing the data, it is observed that the graphs plotting the Nusselt number versus Reynolds number and friction factor versus Reynolds number, derived from both experimental data and the fuzzy logic model, exhibit nearly identical trends with a margin of error of just 3%. This high level of accuracy underscores the reliability of the fuzzy logic model in replicating the experimental results.

CUSTOMER RELATIONSHIP ASSESSMENT USING FUZZY LOGIC

Sustainability is an important approach for all organizations. Social responsibility is also included among the important principles of sustainability. This responsibility involves activities for employees, customers, society and other stakeholders. Communication with customers is important in transport companies. This research evaluates the quality of customer communication within a transport company and proposes improvements following fuzzy logic modeling. The results obtained show that customer relations must be constantly improved. Research limitations refer to the sample of companies involved in the present research.

Fuzzy Logic Model for Determining Optimal Temperature-Humidity Index Values in Dairy Farms in Temperate Climate

ABSTRACT In livestock, temperature, humidity, and Temperature-Humidity Index (THI) affect the welfare, yields, health and viability of animals. This study aimed to develop optimal temperature, humidity, and THI thresholds for dairy farms in temperate climate regions using a fuzzy logic model. THI values were calculated using three different literature-derived equations, considering different temperature and humidity situations in dairy farms. The Mamdani-type fuzzy logic method was utilized to formulate linguistic expressions for temperature, humidity, and THI values. According to the THI thresholds, the areas below the Receiver Operating Characteristic (ROC) were found to be significant (p < 0.001) in all fuzzy algorithms. The study found 100% harmony with the THI thresholds of 66 and 72 for cattle in temperate climates, but only 73.6% harmony with the threshold of 74 for cattle adapted to tropical climate. Briefly, in temperate dairy farms, the fuzzy logic revealed that the optimal temperature, humidity and THI values should be between 14–18.5°C, 65–70% and 52.5–64.5, respectively. However, further research is required to understand the impact of thresholds determined by fuzzy logic on dairy cow production and welfare.

Predicting Extension of Time and Increasing Contract Price in Road Infrastructure Projects Using a Sugeno Fuzzy Logic Model

Road infrastructure plays a crucial role in the development of countries, significantly influencing economic growth, social progress, and environmental sustainability. Major infrastructure projects are frequently challenged by substantial risks and uncertainties, leading to delays, budget overruns, and compromised quality. These issues can undermine the economic viability and efficiency of projects, making effective risk management essential for minimizing negative impacts and ensuring project success. For these reasons, a study was conducted using a Sugeno fuzzy logic system applied to completed projects. The resulting model is based on 10 project characteristics and provides highly accurate predictions for Extension of Time (EoT) and Increasing Contract Price (ICP). By utilizing this model, project management can be significantly improved through more accurate forecasting of potential delays and cost overruns. The high precision of the Sugeno fuzzy logic system enables better risk assessment and proactive decision-making, allowing project managers to implement targeted strategies to mitigate risks and optimize project outcomes.

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%.

Integrating artificial intelligence for optimal thermal comfort: A design approach for electric heating textiles aligned with user preferences

Human thermal comfort, crucial for well-being and productivity, is often improved by personal comfort systems that offer tailored control over environmental conditions while promoting energy efficiency. Previous studies have explored various textile technologies in thermoregulation systems according to user preferences. However, limited research has focused on temperature prediction by artificial intelligence to maximize thermal comfort for varied users. This study proposes a design approach to optimize thermal comfort in electric heating textiles using artificial intelligence, considering user preferences related to age and gender differences. A fuzzy logic model is established as a proof of concept for temperature regulation by varying ambient temperature, followed by developing an artificial neural network model to predict the optimal temperature for maximum comfort. Subsequently, a smart electric heating jacket is fabricated to assess preferred heating temperatures among 50 subjects with varying ages and genders. Results from the artificial neural network model show promising temperature prediction, while subject tests reveal significant differences in skin temperatures based on gender. This emphasizes the need for artificial intelligence-based heating e-textiles to accommodate diverse user needs. The study’s findings are expected to contribute to intelligent temperature regulation in thermal textiles and wearables, benefitting both the industry and consumers through customized heating products.

Fuzzy assessment of process parameter interactions on warpage defect modeling in plastic injection molding

AbstractStudying the interactions among major plastic injection molding process parameters is necessary to understand how they collectively influence major defects such as warpage hence enabling optimization of the process for improved product quality. Existing process parameter interaction studies have used statistical approximations, which have limitations such as reduced predictive power and limited accuracy. To overcome these limitations, this study presents an alternative method of analysis of the interactions among process parameters based on fuzzy logic intelligent algorithm. Through computer aided engineering, factorial design of experiment and fuzzy logic modelling, the study evaluated the effects of major injection molding process parameter interactions on warpage. The results obtained indicated a general increase in warpage with increase in parameters such as melt temperature, mold temperature, injection pressure and cool time whereas an increase in parameters such as ambient temperature and packing pressure decreased warpage. Parameter interactions were obtained both statistically and based on fuzzy logic model and their significance tested through ANOVA. Ambient temperature (30.6%) and melt temperatures (18.7%) had the greatest effect on warpage all with P-values of 0.000 whereas cooling time (0.1%) had the least effect with P-value of 0.250. The largest two way interaction affecting warpage involved melt temperature and cooling time with a contribution of 12.2% whereas the largest three way interaction involves ambient temperature, packing pressure and injection pressure with a contribution of 2.7%. Also, despite cooling time having the least mains effect, most interaction terms with greater effect on warpage involved cooling time and melt temperature. The results from this study provides an insight on targeted injection molding process parameter control for defect minimization.