Implementation Of Fuzzy Logic Research Articles

Implementation of Fuzzy Logic in Determining the Acceptance Status of Fresh Milk Based on pH and Density

Implementation of Fuzzy Logic in Determining the Acceptance Status of Fresh Milk Based on pH and Density

Implementation of Fuzzy Logic in Determining Recommendations for Purchasing Folic Acid Supplements based on Fruit and Vegetable Consumption Patterns in Pregnant Mothers

This research aims to implement fuzzy logic in determining the level of recommendation for folic acid supplements based on the input of the amount of vegetable and fruit consumption and folic acid content. The output of the developed system is in the form of recommendation level with categories of "not recommended", "less recommended", and "recommended". This research data was obtained based on literature studies and through observation of samples of supplements containing folic acid in several related pharmacies. This research method includes direct calculations and the use of MATLAB. The results show that in the example case with the amount of vegetable and fruit consumption of 550 with a folic acid content of 175 can produce an output value of 400 which puts the recommendation level based on dosage in the "not recommended" position. This result has been verified both through direct calculation and the use of MATLAB software. Studies have concluded that the application of using of fuzzy logic can help in providing supplement levels. This contributes to considerate of decision-making in the community, especially in pregnant women, which can help determine the supplements consumed so that the fulfillment of daily consumption of folic acid content can be met.

Multi-response optimization of friction stir welding using fuzzy-grey system

Abstract This study reports that a fuzzy logic-based grey system using Taguchi’s method provides a reliable approach to the prediction of friction stir welding (FSW), which is capable of improving the multi-response performance of butt welds. Using the grey relational database, a fuzzy logic analysis is carried out. Analysis of variance is used to determine the effect of parameters of multi-response behaviors on butt welds. Meanwhile, multiple responses for multivariables are simultaneously optimized. The experimental results show that the fuzzy logic-based grey system using Taguchi’s design reveals the optimal settings of the parameters, which improves the whole properties by more than 28.04%, while the individual properties, such as tensile strength by 13.35%, bending strength by 1.91%, impact strength by 1.05%, and hardness by 0.26%, when compared with the best test in orthogonal arrays. Based on experimental validation tests, excellent agreement between model predictions and experimental results is shown. Notably, the intelligent method proposed is applied to butt welds, and the results of the implementation of fuzzy logic based grey system using Taguchi’s design proved its feasibility and effectiveness with respect to the improvement of the mechanical properties of FSW.

A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

Fuzzy demand-driven material requirements planning: a comprehensive analysis of fuzzy logic implementation in DDMRP

ABSTRACT Demand-Driven Material Requirements Planning (DDMRP) is a new method of inventory control designed to address the challenges of today's complex and volatile supply chain environment. DDMRP replaces traditional Material Requirements Planning (MRP) with a more agile and responsive approach that is based on a set of colour-coded buffers. DDMRP ensures that the right inventory is in the right place at the right time, enabling companies to respond quickly and effectively to changing customer demand. DDMRP requires the setting of various parameters, such as variability factor (VF) and lead time factor (LTF), which can have a significant impact on systems’ performance. Pre-assumed fixed values of DDMRP parameters are used in most of the existing studies. This may lead to either high stockout levels or excess inventory especially in environments involving high level of variability. In this study, we proposed a fuzzy logic-based approach which dynamically adjusts the values of VF and LTF parameters considering demand and lead time variability. The effectiveness of the proposed approach was investigated by comparing its performance with DDMRP based on several numerical experiments. The results showed that the proposed Fuzzy Demand-Driven Material Requirements Planning (FDDMRP) outperforms DDMRP in terms of backorder rate and total cost.

Design of a prototype for sending fire notifications in homes using fuzzy logic and internet of things

This paper highlights the need to address fire monitoring in densely populated urban areas using innovative technology, in particular, the internet of things (IoT). The proposed methodology combines data collection through sensors with instant notifications via text messages and images through the user’s email. This strategy allows a fast and efficient response, with message delivery times varying from 1 to 4 seconds on Internet connections. It was observed that the time to send notifications on 3G networks is three times longer compared to Wi-Fi networks, and in some 3G tests, the connection was interrupted. Therefore, the use of Wi-Fi is recommended to avoid significant delays and possible bandwidth issues. The implementation of fuzzy logic in the ESP32 microcontroller facilitates the identification of critical parameters to classify notifications of possible fires and the sending of evidence through images via email. This approach successfully validated the results of the algorithm by providing end users with detailed emails containing information on temperature, humidity, gas presence and a corresponding image as evidence. Taken together, these findings support the effectiveness and potential of this innovative solution for fire monitoring and prevention in densely populated urban areas.

ANN-Fuzzy Hybrid Control Strategy for MPPT of Grid-connected PV System with Battery Storage under Fast-charging Atmospheric Condition

Abstract: The use of solar energy through photovoltaic arrays is continually expanding and has recently been regarded as one of the cleanest sources of energy. Increasing the amount of electricity provided to the load is one method for lowering the cost of solar systems. Contrarily, altering the load creates a divergence from the maximum power point (MPP) and changes the operating point of the solar conversion system. Due to this, attention has been given to MPPT techniques that can be used with solar systems in numerous research investigations. In this paper, we implement an MPPT method based on an Artificial Neural Network (ANN). This method combined two controllers, ANN and fuzzy logic controller, under the name ANNFuzzy logic hybrid. In the first stage, ANN can determine Vmpp from irradiation and temperature, and both of them are variable. In the second stage, this Vmpp has been corrected by implementing Fuzzy logic in order to minimize the error of the voltage and VMPP. ANN-fuzzy hybrid has been simulated in Matlab-Simulink and was found to be the best solution to follow the maximum power point when irradiation and temperature are varied. The energy extracted from PV is delivered to a battery in order to inject this power into the smart grid by using an inverter controlled by PID. Finally, a LC filter has been used to eliminate the harmonics and compensate for the reactive power. For energy storage, we consider the utilization of lithiumion batteries, which are recognized as an optimal solution for storing energy efficiently. To manage the charge and discharge of the battery, we employ a PID controller and a buck-boost converter. Through this research, we aim to explore the performance and effectiveness of the hybrid ANN-fuzzy simulation for the boost converter system. By combining the capabilities of ANN and fuzzy logic, we expect to achieve improved control and optimization of the power conversion processes in the system. Additionally, the integration of lithium-ion batteries and the use of the PID controller and buck-boost converter allow for efficient management of energy storage and retrieval. Overall, this study investigates the hybrid ANN-fuzzy simulation for the boost converter system, highlighting the integration of various components and control techniques to enhance the performance and efficiency of the system.

Design and Implementation of Fuzzy Logic for Obstacle Avoidance in Differential Drive Mobile Robot

Autonomous mobile robots based on wheel drive are widely used in various applications. The differential drive mobile robot (DDMR) is one type with wheel drive. DDMR uses one actuator to move each wheel on the mobile robot. Autonomous capabilities are needed to avoid obstacles around the DDMR. This paper presents implementing a fuzzy logic algorithm for obstacle avoidance at a low cost (DDMR). The fuzzy logic algorithm input is obtained from three ultrasonic sensors installed in front of the DDMR with an angle difference between the sensors of 45$^0$. Distance information from the ultrasonic sensors is used to regulate the speed of the right and left motors of the DDMR. Based on the test results, the Mamdani inference system using the fuzzy logic algorithm was successfully implemented as an obstacle avoidance algorithm. The speed values of the right and left DDMR wheels produce values according to the rules created in the Mamdani inference system. DDMR managed to pass through a tunnel-shaped environment and reach its goal without hitting any obstacles around it. The average speed produced by DDMR in reaching the goal is 4.91 cm/s.

ODESMAN: Optimizing Decision-Making in Complex Environments: Integrating Neutrosophic and Fuzzy Logic for Advanced System Modeling

Within the domain of complex systems, inherent uncertainties, and ambiguities that traditional models frequently find difficult to handle pose a constant challenge to decision-making. To dramatically improve decision-making frameworks, this study presents a novel methodology called ODESMAN, which synergistically integrates fuzzy logic with neutrosophic sets. Neutrosophic sets, on the other hand, allow one to express the degrees of truth, untruth, and indeterminacy as shifts rather than fixed points. Therefore, their use is more elegant than the existing methods offered. The implementation of fuzzy logic into such sets may provide a high level of effectiveness in managing uncertainty, which can be predicted and quantified. For example, the model allows accounting for uncertainty in the system inputs and processes up to 20%, the variability of truth values 10-50%, and the overall uncertainty 15-30%. The application of the model in practice, specifically in the emergency response, and the supply chain system permitted achieving a 40% increase in flexibility capacity and a 25% improvement in decision-making approaches compared to the traditional frameworks. Therefore, the practical strength and broad utility of the model can be proved, which validates its efficiency and allows broad implementation of this complex theoretical framework into the existing systems.

Implementation of Fuzzy Logic on Watering Automation System for Palm Tree Planting Media Based on WSN

Currently, date palm farmers manually assess soil color and dryness without a definitive reference. Manual watering of date palms persists, although some gardens employ irrigation systems. The absence of communication infrastructure in plantations necessitates flexible network technology. The system comprises a DS18B20 soil temperature sensor and FC-28 soil moisture sensor. Temperature and humidity values identify soil dryness, determining water volume for date palm irrigation. Sensor data is stored on Arduino Uno, undergoes pre-programmed processing, and is then transmitted to the sink node via the LoRa Ra-02 communication module. The results of system testing can be concluded that the temperature and humidity sensors can detect the temperature and humidity of the growing media with a fairly small error rate. The use of fuzzy logic inference and watering automation goes according to plan. It is known that for a soil temperature of 25.31°C and soil moisture of 20%RH, the output volume of watering through the hardware is 15,31 liters. The communication module used as a data sender can also run well and stable at a range of up to 200 meters with RSSI = -105 and SNR = -7,1.

APPLICATION OF FUZZY LOGIC IN THE CONTROL SYSTEM OF ELECTROMECHANICAL EQUIPMENT

The article considers the option of solving the problem of automatic selection of settings of proportional-integral-differential (PID) regulators to control the thermal process with chaotically changing dynamic properties. Based on analytical and experimental data, it illustrates the influence of various factors on changes in the dynamic properties of electromechanical equipment (EME) when heated during operation. The work of classical and fuzzy PID controllers was investigated using simulation models in Matlab and Simulink environments. The article illustrates the expediency of changing the configuration charts of the PID regulator when controlling the process, which can be carried out with the help of a phasing unit that implements variants of the rules for controlling the signals of the charts. The Control System ToolboxTM application was used to determine the application rules for adjusting electrical values. The attractive nature of the changes in the transition processes reflects the graphical and tabular data. Transient processes in a nonlinear control algorithm (NCA) are characterised by improved parametric characteristics. To increase the accuracy of the chart settings, it is advisable to use data from actual thermal events that determine perturbing variables. Realisation of the above-mentioned intelligent device for automatic setting of the real-time regulator, providing the formation of a more accurate output of the control signal, determines the advantages of the proposed engineering solution and the feasibility of its use at similar technological facilities. In the article, the practical implementation of fuzzy logic with the help of the Arduino family microcontroller is recommended. In addition, the stand-alone NCA unit can be used with an additional actuator for smoother thermal control during the operation of EM components, while maintaining the same basic automation system with a classic PID controller. Keywords: electromechanics, automation, controller, microcontroller, modelling, electronic model, fuzzy logic, algorithm, controller settings.

Temperature and Humidity Control System With Long Range in Mushroom Barn Using Fuzzy Logic

Room temperature and humidity are parameters required for quality mushroom growth. To get the appropriate and stable temperature and humidity values, a control system is needed. This test aims to create a temperature and humidity control system using IoT-based fuzzy logic implementation. This temperature and humidity control system, uses the main components of DHT22 sensors, NodeMCU ESP32, Arduino nano and Lora. The transmitter is placed in the mushroom barn. The transmitter control system stabilizes the temperature and humidity in the barn using a predetermined fuzzy method. After that, the data processed at the transmitter will be sent to the receiver via Lora communication. At the receiver, the device is placed in the mushroom farmer's house, which will capture the data sent by the transmitter. This recognition system has a good level of accuracy against the DHT22 sensor and Hygrometer, which has a difference of 1℃ or a difference of 2.08%. In contrast, for humidity, it has a total difference of 16% or 97.61%. When testing using fuzzy logic, the resulting temperature and humidity are stable, and the predetermined fuzzy logic carries out watering.

Klasifikasi Penyakit Gangguan Jiwa menggunakan Metode Logika Fuzzy

Purpose: This research aims to facilitate psychologists in handling individuals with mental disorders by categorizing them based on their symptoms and conditions using fuzzy logic, which mimics the functioning of the human brain.Design/methodology/approach: The categorization is performed by applying Mamdani fuzzy logic, designed in consultation with psychology experts. Ten initial symptoms each have parameters (Mild, Moderate, and Severe) as input variables, and the output variable involves mental health disorders such as Schizophrenia, Bipolar disorder, Eating disorders, and Anxiety. The fuzzy process employs the Mamdani method with IF-THEN rules and AND operators. The implementation of Mamdani fuzzy logic achieves adequate accuracy in classifying individuals with mental disorders, providing a strong foundation for a more targeted psychological approach. In the context of accuracy, fuzzification analysis for each health disorder can offer further insights.Findings/result: Results of the study for Schizophrenia, for instance, show a fuzzy diagram membership of approximately 0.4, indicating a potentially high level of thought impairment and interpersonal skills. Weighting for low, medium, and high is then assessed to categorize patients. A similar process is undertaken for Bipolar disorder, with special attention to the middle value and the strong relationship between two input values. Regarding mental illness, membership analysis indicates an increasing level of membership corresponding to condition groups, suggesting compatibility with existing rules.Originality/value/state of the art: These findings reinforce the Mamdani fuzzy logic implementation as a reliable approach in classifying individuals with mental disorders, with the potential to enhance psychological diagnosis and interventions more effectively

Implementation of Fuzzy Logic in Detecting Air Temperature Based on Microcontroller

The environment, technology, and many facets of daily living are all significantly impacted by temperature conditions. Knowing environmental health, agriculture, industry, electrical technology, weather and natural disasters, transportation, and even scientific research are just a few of the many reasons why temperature conditions are crucial. Fuzzy algorithms are used to more flexibly process temperature data while creating a temperature detector employing fuzzy logic. We can base our conclusions on a wider range of criteria than merely right or wrong thanks to fuzzy logic. A temperature sensor, microprocessor, and display (such an LCD or LED screen) are some basic electronic components that can be used to create a temperature detector.

Fuzzy current analysis-based fault diagnostic of induction motor using hardware co-simulation with field programmable gate array

Introduction. Presently, signal analysis of stator current of induction motor has become a popular technique to assess the health state of asynchronous motor in order to avoid failures. The classical implementations of failure detection algorithms for rotating machines, based on microprogrammed sequential systems such as microprocessors and digital signal processing have shown their limitations in terms of speed and real time constraints, which requires the use of new technologies providing more efficient diagnostics such as application specific integrated circuit or field programmable gate array (FPGA). The purpose of this work is to study the contribution of the implementation of fuzzy logic on FPGA programmable logic circuits in the diagnosis of asynchronous machine failures for a phase unbalance and a missing phase faults cases. Methodology. In this work, we propose hardware architecture on FPGA of a failure detection algorithm for asynchronous machine based on fuzzy logic and motor current signal analysis by taking the RMS signal of stator current as a fault indicator signal. Results. The validation of the proposed architecture was carried out by a co-simulation hardware process between the ML402 boards equipped with a Virtex-4 FPGA circuit of the Xilinx type and Xilinx system generator under MATLAB/Simulink. Originality. The present work combined the performance of fuzzy logic techniques, the simplicity of stator current signal analysis algorithms and the execution power of ML402 FPGA board, for the fault diagnosis of induction machine achieving the best ratios speed/performance and simplicity/performance. Practical value. The emergence of this method has improved the performance of fault detection for asynchronous machine, especially in terms of hardware resource consumption, real-time online detection and speed of detection.

Implementation of Fuzzy Logic in Soil Moisture and Temperature Control System for Araceae Plants Based on LoRa

The Araceae plants are highly popular among plant enthusiasts worldwide. The Araceae family has more than 100 different types and thousands of species. Despite ever-evolving times, these plants have a high market value and challenging breeding methods. To achieve optimal quality, it is important to maintain the appropriate humidity and temperature according to the natural conditions of these plants in the tropical rainforest. The Node MCU ESP32 is a processor for instructions from the room temperature sensor, room humidity sensor, and soil moisture sensor. Additionally, this component controls the blower and misting system as output, which will be processed through LoRa technology to transmit monitoring data to the Blynk software. This study utilizes fuzzy logic to categorize room temperature, humidity, soil moisture, and output results for different Araceae plants. LoRa technology is used to send monitoring data efficiently in the data transmission process. During data retrieval using long-range technology, a delay of approximately 5 seconds is known between the receiver and transmitter at a distance of 700 meters. Constraints that cause issues with this long-range technology are influenced by wind, which affects antenna signal strength, and the presence of trees and buildings as obstacles. The monitoring results show an average temperature in normal conditions and an average humidity in wet conditions. At the same time, soil moisture is monitored to maintain normal humidity, resulting in all outputs being off.

Implementasi Fuzzy Logic Pada Sistem Kontrol pH Air Mineral Berbasis IOT

Implementation of Fuzzy Logic in the IOT-Based Mineral Water pH Control System for various existing bottled mineral water products, this system can measure the pH value, to find out if the value is still within the limits suitable for consumption or not based on government regulations. The public finds it challenging to understand the level of the pH value of the product because the current state of information regarding the pH level of mineral water generally is not listed in the mineral water bottle circulating on the market. Hardware design, application design, and hardware and software integration were the three steps of this research project. The pH value will be read by this control system from the output of the sensors then the data is collected into a data set. The data will be examined for trends using fuzzy logic, which will be used to classify the maximum and minimum pH levels, acidity levels, and base levels. The study's findings show that an internet-based web of things can access the mineral water pH control system to ascertain each mineral water product's pH value and temperature. This information can then be used by consumers to ascertain the pH level of each mineral water product.

Use of Fuzzy Logic in Cyber Security System: Cyber Threat Intelligence

Purpose of the study: Fuzzy logic is a mathematical concept that allows for the handling of uncertain or imprecise data. In cyber security, fuzzy logic can be used to improve the accuracy and efficiency of security systems. Cyber threat intelligence plays a crucial role in modern cybersecurity by enabling organizations to proactively identify, analyse, and respond to potential cyber threats. With the increasing complexity and sophistication of cyber threats, effective cyber threat intelligence has become a critical component of modern cybersecurity. Traditional approaches often struggle to handle the inherent uncertainties and complexities in threat intelligence data.
 Methodology: Through a comprehensive literature review, examines the current state of cyber threat intelligence and the principles of fuzzy logic. The paper presents a detailed analysis of how fuzzy logic can be applied to various aspects of cyber threat intelligence, including threat modelling, detection and classification, risk assessment, and decision support systems.
 Main Findings: The findings highlight the benefits of using fuzzy logic in cyber threat intelligence and pave the way for further research and development in this promising field. The future prospects and challenges of integrating fuzzy logic with other advanced technologies such as machine learning and artificial intelligence.
 Applications of this study: The proposed approach has the potential to significantly improve the analysis and decision-making processes in agriculture, helping farmers to make more informed decisions about crop treatments and ultimately increasing crop yields.
 Novelty/Originality of this study: The outcomes of this research contribute to advancing the field of cyber threat intelligence and provide valuable insights into the practical implementation of fuzzy logic for better threat analysis and mitigation strategies.

Comparison of Control Method System by Using Perturbation and Observation Algorithms and Fuzzy Logic for the Maximum Power Point Control in Photovoltaic Systems

Currently, photovoltaic energy conversion has developed tremendously in the global energy industry. The enormous capacity of solar power plants generates electricity all over the world. Hundreds of gigawatts of electrical energy are supplied to the networks of various countries around the globe. In addition, up to hundreds of gigawatts of installed capacity of solar power plants are commissioned every year in all countries of the world. And this has been the trend for the past few decades. The main problem of solar photovoltaic conversion is the instability of the solar radiation flux associated with the climatic conditions in the regions where photovoltaic plants are operated. This problem can be solved by using highly efficient methods to control the generation of solar power plants, in particular, such as the use of information control systems for a particular PV system to increase the final generation of electrical energy supplied to the consumer. The photovoltaic (PV) conversion of solar radiation flux is an important renewable energy source. Due to the varying intensity of the sun, the electricity generated by PV directly from the radiation flux is not constant. The photovoltaic system currently uses maximum power point (PMP) tracking in perturbation and observation (P&O) mode to increase the final output power of the photovoltaic panels. A step-down DC-DC converter allows PMP to change the PV operating voltage and determine the maximum power output of the PV panel. This study proposes a fuzzy logic implementation. The magnitude of the perturbed voltage is determined by the change in power dq and the change in power relative to the change in voltage dq/dv is fuzzy. The performance of fuzzy logic is investigated in this work in order to optimize MPM. Fuzzy logic can simplify maximum power tracking and reduce voltage instability. According to the simulation results, the MPM method based on fuzzy computation performs better than the traditional P&O method. By using the proposed methods, we can significantly improve electric power generation and increase the efficiency of photovoltaic conversion.

Implementation of Fuzzy Logic for Chili Irrigation Integrated with Internet of Things

Chili, mustard greens, and tomatoes have always been farmers' favored crops, despite their high water and labor demands. Adapt to these conditions by utilizing smart agriculture systems (SAS) agricultural techniques that involve technology such as automatic irrigation that regulates watering based solely on routine, regardless of land conditions. This type of control during the transitional season can lead to root rot and fungisarium disease on chile plants. In the form of an embedded system with internet of things (IoT) monitoring, a system incorporating artificial intelligence such as fuzzy logic is proposed as a solution. Fuzzy logic will regulate irrigation based on the land's humidity and temperature using computational mathematics. Beginning with the fuzzyification stage to map the sensor's temperature and humidity input values, fuzzy logic is applied. The creation of an inference engine in the NodeMcu 8266 microcontroller to interpret fuzzy rule statements in the form of aggregation of minimum conditions with the AND operator, followed by the combination of a single set value of 0 and 1 in the fuzzy system to produce an appropriate actuator response After the entire system has been prototyped, testing is conducted to determine the responsiveness of the fuzzy program code to changes in the simulated agricultural cultivation land ecosystem. This study found that the fuzzy logic program code embedded in the nodeMCU8266 microcontroller effectively controls the spraying duration of the pump in response to various simulated environmental conditions within 3.6 seconds.