Application Of Fuzzy Logic Research Articles

Application and Development of Embedded Systems with IoT Components: Aspect of Safety and Reliability

We will present the possibilities of application and development, especially in the field of embedded systems, its interaction with other IoT components, the security aspects of individual components, as well as the domain of their interaction. In embedded systems, we will present new technologies such as fuzzy logic, application possibilities in embedded systems, and machine learning, as application possibilities through the implementation of machine learning. Then we will describe some more examples of the application of fuzzy logic, the automatic control of certain functions in cameras, as well as the defuzzification process, and the possibility of application in security cameras. In embedded systems, we will present the basic aspects of optimization and security, both in everyday applications and in interaction with other components of IoT technologies. The paper shows how security, reliability, and cost estimates affect the implementation phases and the final use of embedded systems, through examples of their application in industry. Security and data protection is shown through the construction of the mentioned devices, their application, but also different encryption methods, permissions, security devices at the network level, as well as implemented IoT technologies. Application examples are focused on the real segment, both in the field of transport, multimedia, design, and in the field of industrial application possibilities.

Application of an intelligent fuzzy logic based sliding mode controller for frequency stability analysis in a deregulated power system using OPAL-RT platform

Owing to unpredicted power demand, integration of distributed generators and parametric disparity in the system cause the power system more intricacy which in turn affects the frequency and power fluctuation critically. To mitigate these phenomena, an intelligent and efficient automatic generation control (AGC) is indispensable. Therefore, in this paper, an intelligent and robust controller named fuzzy sliding mode controller (FSMC) has been recommended to tackle the AGC problem in a deregulated power system in the presence of distributed generators effectively. Again, to manifest its dominance over SMC, fuzzy-PID and PID controllers, dynamic response of the deregulated system under Poolco, Bilateral and Contract violation conditions has been evaluated and compared. Further, to support the FSMC controller’s ability, another test model has been taken for the AGC study. The gains of these controllers have been enumerated by Gannet Optimization Algorithm (GOA) subjecting to a minimization problem. Exploration phase of GOA involves two steps, namely U- & V-shaped diving patterns of Gannets and exploitation phase again involves two steps namely sudden rotation and random walk of Gannets. These four steps ensures quick convergence of the objective function to its optimal value. Apart from transient response under different power transaction conditions, parametric uncertainties of the system, solar and wind power variation and abrupt load change have been considered to support the robustness & credibility of FSMC controller. In addition to this, the stability of the proposed model has been analyzed in the frequency domain. Finally, the MATLAB/SIMULINK based transient response has been validated by a real-time simulator using OPAL-RT-4510, Xilinx Kintex-7 FPGA software.

Penerapan Artificial Intelligence Menggunakan Fuzzy Logic dalam Dunia Pendidikan

This research aims to apply artificial intelligence or what is commonly known as Artificial Intelligence (AI) using fuzzy logic in the world of education. The topic of artificial intelligence has become a hot and important topic of conversation today. Fuzzy logic allows processing unclear information and is used to overcome uncertainty that exists in the world of education. This article discusses the use of fuzzy logic techniques to improve the quality of education. One application of fuzzy logic is in the development of an automatic assessment system used by lecturers in providing assessments to students in the learning process. Fuzzy logic can carry out assessments objectively and enable more appropriate curriculum adjustments based on students' interpersonal skills which can ultimately increase the efficiency of the learning process. The use of fuzzy logic can provide recommendations for the learning process that can help students find material that suits their interests and needs. Fuzzy logic can be used to measure student development in social and emotional cognitive aspects. The application of AI with fuzzy logic aims to improve the quality of education and student learning experiences in the world of education.

SIMULATION OF OPTIMAL MIX OF SIO2-TIO2-AL2O3 NANO ADDITIVES FOR THE MINIMAL WEAR AND COEFFICIENT OF FRICTION OF LUBRICANT USING FUZZY LOGIC

Nano-particles of oxides of various metals are typically added with the base lubricants for enhancing the tribological properties, especially the wear and coefficient of friction of the lubricants. However, it is required to determine the optimum proportion of mix of the nano-additives in order to obtain the desirable characteristics gained through the addition of various oxides of metals mixed with the lubricants. This paper deals with the application of fuzzy logic to simulate the various proportions of a mix of three nano-additives of SiO2, Al2O3 and TiO2 with the corresponding predicted values of tribological characteristics of wear and coefficient of friction. The limited number of outcomes obtained from a full factorial design of experiments carried out with these three nano-additives at three different levels of mixing, are used as inputs to the fuzzy logic simulation. This approach facilitates simulating as many combinations of nano-additives as possible with the base lubricant oil along with the predicted output of characteristics of interest, which otherwise will be cumbersome to carry out the experiments for all the combinations of mix of additives.

Analyzing Criteria Affecting Decision-Making Processes of Human Resource Management in the Aviation Sector - A Fuzzy Logic Approach

In today's fast-paced and ever-changing business landscape, effective decision-making is paramount to achieving success and maintaining a competitive edge. This holds particularly true in the aviation sector, where Human Resource Management (HRM) plays a pivotal role in optimizing workforce performance and ensuring operational efficiency. However, HRM decision-making processes are often confronted with multifaceted challenges that encompass various criteria and encompass both objective and subjective factors. To tackle this complexity, a novel and adaptive approach is needed. In this study, we employ a Fuzzy Logic Approach to analyze the criteria influencing decision-making processes in HRM within the aviation sector, aiming to provide a comprehensive and flexible decision-support system for HRM practitioners and contribute to the sector's overall performance and success. The contribution of this study lies in its innovative application of Fuzzy Logic to HRM decision-making in the aviation sector. By capturing the inherent uncertainties and vagueness that HRM practitioners encounter, the proposed Fuzzy Logic-based model offers a more robust and context-sensitive decision-support system. Based on the Fuzzy Logic application and sensitivity analysis, the findings reveal the significance of employee satisfaction as the most influential criterion in HRM decision-making within the aviation sector. The Fuzzy Logic model demonstrated a strong positive correlation between high employee satisfaction levels and favorable HRM Decision Outcomes. This finding emphasizes the pivotal role of employee satisfaction in shaping HRM strategies and outcomes within aviation organizations.

A Review of Fuzzy Logic Method Development in Hydraulic and Pneumatic Systems

Fuzzy logic has been developed since the 1960s. Research related to fuzzy logic application in hydraulics and pneumatics is mainly aimed at energy demand reduction and improvement in operational characteristics. This article summarizes the recent achievements in hydraulic and pneumatic fuzzy logic system design. First, the main application areas have been identified, including control and fault diagnosis. The control systems were additionally grouped according to the main objects of study, such as pumps, actuators, proportional valves, etc. Then, the results of the recent research were presented, and the main features of the designed fuzzy logic units were summarized for each group. Particular attention was paid to types of membership functions used for fuzzification and defuzzification, numbers of fuzzy sets defined for input and output signals, types of fuzzy operators, the applied inference algorithm and the defuzzification method. Based on the analysis of the listed parameters, conclusions were formulated regarding advantages, main issues and difficulties, as well as recommended directions for further development.

Smart Shrimp Farming Using Internet of Things (IoT) and Fuzzy Logic

In the case of ponds with Litopenaeus Vannamei shrimp, water quality parameters play a significant role in shrimp growth. Leveraging technology enhances water quality to optimize growth and survivability in the shrimp farming industry. The research aimed to empower local farmers with smart shrimp farming technologies, including Information Technology (IT), such as the Internet of Things (IoT), and Fuzzy Logic. The research also involved a comparison between Litopenaeus Vannamei shrimp in two different aquariums: one serving as a control group and the other implementing IoT and Fuzzy Logic for a period of 30 days. The initial Litopenaeus Vannamei shrimp stocking was 135 shrimps for control aquariums and 132 for experimental aquariums. Then, the research used Arduino ESP 8266, Raspberry Pi 3, and SciKit-Fuzzy library to record and process the data. Through the application of IoT and Fuzzy Logic, the research successfully increases survivability by 6%, specific growth rate by 28%, and length by 8% in 30 days compared to conventional methods. The results highlight the potential use of technology in Litopenaeus Vannamei shrimp farming. The proposed system’s hardware and software architecture can be easily scaled to accommodate the needs of Litopenaeus Vannamei shrimp farmers with multiple ponds, offering flexibility and adaptability.

Correlation Coefficient in Interval Valued Fermatean Picture Fuzzy Set for Crop-Based Bio-ethanol Production

This comprehensive review explores the critical aspects of bio-ethanol production in Tamil Nadu, focusing on three primary feedstocks: Sugar Cane, Corn, and Sweet Sorghum. The analysis delves into key criteria such as yield, environmental impact, and cost-effectiveness, providing valuable insights for stakeholders, researchers, and policymakers. Additionally, the study investigates the application of fuzzy logic in bio-ethanol production, emphasizing its role in addressing uncertainty and imprecision. This research serves as a vital resource for understanding the nuances of bioethanol production, enabling informed decision-making for a sustainable energy future in Tamil Nadu and beyond.

FUZZY DISSOLVED OXYGEN CONTROLLER APPLIED IN ACTIVATED SLUDGE PROCESS

Roughly 80% of waste water treatment plants are based on active sludge technique due to its simplicity and reliability. Under aerobic conditions microorganisms can grow on some organic pollutants present in the wastewater. As such, dissolved oxygen control is paramount in order to save energy, improve effluent quality and achieve high nitrogen removal. However, due to the nonlinearities of bioprocesses, dissolved oxygen (DO) is a challenge in terms of variable control. In Suzano municipal Waste Water Treatment Plant (WWTP) - located in São Paulo Metropolitan area - dissolved oxygen control is conducted based on a set of heuristic rules stablished by skilful operators. Although yielding satisfactory results, staff training is time consuming. Under the light of these facts, this study aimed to develop a fuzzy DO controller applied to activated sludge process. Data collection and implementation of the fuzzy control system were conducted in bioreactors at Suzano WWTP. Three fuzzy inference systems were developed: “aerator number”, “aerator speed” and “submergence height”. The fuzzy controller was tested and calibrated with a dynamic simulator and implemented as a supervisory controller, yielding positive results. Statistically, dissolved oxygen mean was 1.75 mg/L with a standard deviation of 0.25 mg/L. Based on the results the application of fuzzy logic for DO control in activated sludge process has proven not only viable, but an interesting control option. Fuzzy logic overcame DO non-linearities by translating an empirical knowledge into a linguistic, rule-based controller. It is a promising technique, bringing significant advances, both in treatment performance and energy efficiency.

Method Provides Real-Time Anomaly Detection for Drilling-Fluid Properties

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 212443, “Real-Time Anomaly-Detection Methodology for Drilling-Fluid Properties,” by Moacyr N.B. Filho, Thalles P. Mello, and Cláudia M. Scheid, Federal Rural University of Rio de Janeiro, et al. The paper has not been peer reviewed. _ Online drilling-fluid-measurement technologies have become an essential tool for drilling automation. While online density measurements are widespread, the availability of rheology measurements is increasing quickly, and additional properties are central to ongoing field trials. The complete paper presents the concept of supervisory and advisory systems dedicated to support the detection of abnormal events and to provide guidelines for fluid treatment. Introduction The Laboratory of Fluid Flow of the Federal Rural University of Rio de Janeiro and Petrobras developed a flow loop for the proposal and validation of online sensors for the measurement of drilling-fluid properties. In previous work, the authors described the development of a set of online sensors to measure fluid properties with control of operational conditions of temperature, pressure, and fluid flow rate. Following previous research, this study proposes a new method using a technique based purely on data to diagnose and detect deviations in drilling-fluid properties. The method proposed applies principal component analysis (PCA) for detecting anomalies in drilling fluids. Method PCA-Based Anomaly-Detection Technique. This method consists of the linear transformation of the space of monitored variables, which generates a new space of variables called principal components, in which each principal component is a linear combination of the process variable. This allows the new space of variables to be linearly independent—that is, the principal components are linearly independent, allowing the calculation of process monitoring statistics. The ability to detect anomalies in drilling-fluid properties is necessary to train the system in the normal state of operation. The training based on the normal state of operation was performed through the calculation of the principal-component matrix and the selection of the reduced principal-component matrix. Once the process model was trained, the monitoring of new samples was performed with the help of two statistical tools that described the health of the process operation: T² and Q statistics. The T² statistic measures variations in the main space. T² only detects variation in the subspace of the first principal components larger than those attributed to the dynamics of the process. The Q statistic denotes the change of events not explained by the principal-component model. It is a measure of the difference, or residual, between a sample and its projection on the model. The failure-detection procedure based on the use of PCA consisted of continuous assessments of whether process statistics were within or outside an acceptable range. A method previously proposed in the literature used control systems based on fuzzy logic to monitor properties of drilling fluids and propose corrections, which are performed automatically by an experimental unit containing actuators for additions of drilling agents as thickeners and viscosifiers. The application of fuzzy logic depends on inference rules, which must be provided to the system by the programmer. This is a limitation of corrective systems based on fuzzy logic.

Experiment of Fuzzy Logic on Game System

Abstract: Applications of fuzzy logic in game creation have proven useful, especially when it comes to modelling the behavior of non-player characters (NPCs) in video games. NPC behavior in video games has historically been governed by strict rule-based systems or scripted sequences, which frequently led to restricted and predictable interactions. On the other hand, fuzzy logic adds a degree of flexibility and ambiguity to the decision-making processes of NPCs. By improving the naturalness, adaptability, and emotional resonance of NPC behavior, fuzzy logic may greatly enhance player experiences in games. It provides players with progressively more engaging and dynamic gaming experiences as it continues to be incorporated into game production, opening up new possibilities for interactive narrative, gaming, and immersive virtual worlds. Based on Utku Kose's article, "Developing a Fuzzy Logic Based Game System," I have investigated the use of fuzzy logic in gaming contexts for NPC decision-making in this study.

Application of Mamdani's Fuzzy Logic in Determining the Price of Silk Fabric in Ujung Pandang District

The development of the silk textile industry in Indonesia is supported by several provinces including South Sulawesi. South Sulawesi has several centers for the development of mulberry and silkworm cultivation which are the basic ingredients for making silk cloth. Silk cloth is one of the processed products from silk thread which is produced from cocoons produced by silkworm cultivation and woven into silk cloth. Traditional woven fabrics produced in regions in Indonesia, including South Sulawesi, are not only made for clothing purposes. The purpose of this study is to determine the price of silk cloth by applying the Mamdani fuzzy method to the MATLAB software, so that silk cloth businesses can set prices according to the quality of the goods. Fuzzy logic is considered capable of mapping an input into an output without ignoring the existing factors. Based on this, fuzzy logic will produce a model of a system capable of estimating selling prices.

Application of fuzzy model for the shelf-life determination of whole sea bass (Dicentrarchus labrax) under refrigerated temperatures

In this study, microbiological deterioration of whole sea bass stored at refrigerator temperatures (4°C) was determined through fuzzy modeling. Total Mesophilic Aerobic Bacteria (TMAB), Total Psychrophilic Aerobic Bacteria (TPAB), Lactic Acid Bacteria (LAB), Pseudomonas sp., and Enterobacteriaceae counts were observed during 12 days of storage. Quality Index Method (QIM) was used for sensory analysis of the samples and shelf life was determined according to sensory deterioration. The growth of the microorganisms was statistically significant (p<0.05) at the end of the storage period. Additionally, significant differences (p<0.05) were observed for QI scores after the 8th day of the storage in whole sea bass stored under refrigerated conditions. A total of 12 rules were determined in the fuzzy logic application. Mamdani was used as the fuzzy type for fuzzy modeling, and centroid was used as the defuzzification method. Fuzzy membership values were computed using triangular membership functions. According to the research results, the shelf life of sea bass fish was determined as 8 days from the time of 12 days storage. As a result, it was observed that the sensory score could be determined by using Fuzzy logic (R2=0.96) according to microbiological parameters in sea bass stored in refrigerator conditions. In this context, it was concluded that Fuzzy logic could be used in shelf-life estimation in seafood products.

Penerapan Metode Fuzzy Sugeno Untuk Menentukan Kelayakan Pengiriman Limbah Barang Berbahaya Dan Beracun

PT Semesta Citra Alam Batam is engaged in waste management and is located in the city of Batam. The waste collected by PT Semesta Citra Alam Batam is gathered at a waste collection warehouse located in the Industrial Waste Management Area of Batam City. The collected waste must be transported to waste utilization or hazardous waste disposal facilities. The data collection technique in this research is through observation and interviews conducted at PT Semesta Citra Alam Batam. The data analysis method used is fuzzy logic with the Sugeno method, which involves four stages: fuzzification, forming a fuzzy knowledge base, inference engine, and defuzzification. The research results from the application of fuzzy logic to determine the shipment of hazardous waste (B3 waste) at PT Semesta Citra Alam Batam involve both input and output variables. The input variables consist of load, content, and the output variable is a decision made using Matlab. These variables will generate rules that will be used to determine whether to send the waste or not.

Метод нахождения динамических приоритетов грузовых операций для оптимизации работы самоходных подвижных единиц в железнодорожных промышленных транспортно-технологических системах

The application of fuzzy logic is considered in optimizing the sequence of operations that involve the use of self-propelled units in industrial railway transportation. The consequences of using constant values calculated analytically as operation priorities have been analyzed. The type of queue discipline and the specifics of constraints on the stay of requests in the queue, characteristic of railway industrial transport and technological systems, are described. The advantages and disadvantages of existing methods for queuing freight operations specified in the daily work plan in railway industrial transport and technological systems are investigated. Limitations arising from the use of classical scheduling theory methods have been identified. Finding the optimal sequence of operations in railway industrial transport and technological systems involves simultaneous movement planning for multiple mobile units, which is not considered in classical graph theory. The classical problem of finding the shortest path and its known solving algorithms only work with static graphs, while railway industrial transport and technological systems are characterized by rapid changes in the graph's state. The problems caused by the dynamic nature of the graph's state in railway industrial transport and technological systems have been analyzed. The application of fuzzy logic methods will enable the construction of an optimal sequence of operations based on incomplete and imprecise information and address several operational planning tasks for freight operations without precise calculations. The expected result of applying the method for determining dynamic operation priorities implemented in the neuro-fuzzy module is an increase in the adaptability of operational planning for freight operations and a reduction in the time of engagement and the required number of self-propelled mobile units.

The application of fuzzy logic techniques to improve decision making in apparel size

Traditional set theory, or crisp set theory, is built on the concept of crisp sets. These are sets for which the membership of an element within a set is defined as either true or false; in or out; 1 or 0. This construction is extremely useful, as mathematics has shown, but it struggles to model concepts of our world that possess vagueness or uncertainty. Therefore, we explore an expansion of set theory to allow an element to be partially within a set, thus constituting what is known as a fuzzy set. This paper introduces the basic concept of fuzzy sets, which includes fuzzy sets and crisp sets, as well as the operations of a fuzzy set and fuzzy classification systems. Fuzzy logic has been utilized to solve numerous textile-related difficulties, one of which was determining the proper clothing size. In this study, we examined fuzzy logic applications in textiles, such as the construction of fuzzy expert systems and fuzzy logic for predicting clothing size. This research demonstrates that when determining the correct size of clothing, the outcome is heavily reliant on fuzzy logic.

Investment Decision Making in Digital Business Using Tsukamoto Fuzzy Logic

This research investigates the application of Tsukamoto's Fuzzy Logic in investment decision making in a digital business context. By integrating human knowledge and numerical data, this method seeks to overcome the challenges of complexity and uncertainty that often arise in the fast-changing digital business environment. Through analysis of case studies and interviews with industry practitioners, this study identifies the steps for implementing effective Tsukamoto Fuzzy Logic, including the formation of fuzzy variables, determination of membership functions, application of fuzzy rules, and defuzzification processes. The results of Tsukamoto's Fuzzy Logic calculations are applied to digital investment cases, providing an overview of investment quality based on various input variables. This research shows that this method can produce a holistic and informative approach in making investment decisions. In addition, the diverse participation of practitioners in various regions provides valuable insights in dealing with the uncertainties of digital business. In this challenging digital era, this research provides guidance for decision makers in dealing with the complexities of a dynamic business environment.

Decision Fusion Using Fuzzy Logic Type 1 for Two Aviation Scenarios

In this paper we discuss some aspects of decision fusion using fuzzy logic of type 1 for two aviation scenarios. We study the fuzzy inference system/engine and briefly discuss the MATLAB-GUI based tool developed by the authors to evaluate any existing or new fuzzy implication functions. Two examples: i) formation flight, and ii) flying along an air lane are numerically evaluated and the performance of decision fusion systems incorporating fuzzy logic is illustrated. The results indicate that some existing as well as new implication functions, devised by the authors, work very well for the examples considered. This opens up new possibilities of applications of fuzzy logic based decision fusion to varieties of aerospace/aviation problems in data fusion, decision making, and decision fusion.

Investigation of enterprise economic management model based on fuzzy logic algorithm

With the development of modern society and economy, modern business models have changed. Enterprise management is no longer the main function, which requires new management concepts to coordinate production and operation and create new management models. Corporate economic management is one of the main tasks of corporate governance. In order to improve corporate governance, the effective corporate economic evaluation model must be established. The advantage of fuzzy logic algorithm is that it can effectively express fuzzy data and is easy to understand, which is more transparent. The application of fuzzy logic algorithm in the research of enterprise economic management can fully reflect the advantages of fuzzy logic algorithm and improve enterprise governance. In this paper, the fuzzy logic algorithm was used to study the economic management model of enterprises. This paper first introduced the enterprise economic management model, including the definition, characteristics and shortcomings of the enterprise economic management model. The performance evaluation methods of enterprise economic management were introduced, including the clarity of performance evaluation objectives, the optimization of performance evaluation means and the feedback of performance evaluation results. Finally, the fuzzy logic algorithm was used to evaluate the performance of enterprise economic management, and the indicators and performance evaluation model of enterprise economic management were established. In the experimental part, the fuzzy logic algorithm was used to evaluate the economic management of four enterprises. The experimental results showed that Enterprise A had the highest ability to evaluate the economic management of enterprises, and the comprehensive evaluation result of economic management was above 0.9, so it had a very high enterprise economic management ability. The application of fuzzy logic to the study of enterprise economic management could directly show the advantages and disadvantages of the enterprise economic management model through quantitative analysis of the enterprise economic management model. This enabled enterprises to understand the current economic management situation and improve it based on the facts, which helped enterprises to increase their economic management ability.

Inventory Management Under Uncertainty Condition with Fuzzy Logic: A Literature Review

Fuzzy set theory, or fuzzy logic, has been in use in inventory systems since the 1980s, with vaguely defined, ill-defined, or imprecise values, or decisions based on individual subjective beliefs. Provides a framework for describing the parameters that can be passed. There are many uncertainties in inventory management that can arise due to many things, such as: Order Changes, Random Supplier Capabilities, or Unexpected Events. Fuzzy logic as a method of inventory control that provides a framework by considering parameters that are vague or poorly defined, or whose values are imprecise and determined based on individual subjective beliefs make it available. The purpose of this white paper is to review previous studies using fuzzy logic in inventory management to see the uncertainty variables used. The method used is a thematic analysis of articles on the application of fuzzy logic in various industries. The results of the review show that variables like fuzzy logic make it easy to obtain results from uncertainty variables that can be applied to industrial activities to enable manufacturing activities to be carried out effectively and efficiently increase.