Fuzzy Logic Method Research Articles

Perspectives of the Low Force Friction Welding Process

The conventional solid-state friction welding process involves imparting a movement to one of them, bringing them closer together so that there is friction from the clamping force. By overcoming the frictional resistance on the surface of the workpieces, work converted into heat is generated. The obtained heat heats the elements to a temperature close to the melting point but not exceeding it. After stopping the movement in relation to each other, the process of pressing the elements with the force P with a greater force causes plasticization of the material and the formation of a flash. In low pressure friction welding, most of the heat required for the joining process comes from the induction coil. This means that two key process parameters such as friction time and contact force are significantly reduce. This affects the course of the process and the end result of the process of joining materials. The shape and size of the flash as well as the size of the heat-affected zone in the weld will change. Among the many advantages of this method of joining metals, one should mention the possibility of welding smaller parts, thin-walled, with complicated geometry, which the friction butt welding process would not be able to cope with. Additionally, there is a possibility of heat treatment. In order to verify the feasibility of the friction welding process with low pressure in industrial conditions, a number of tests presented in this study were carried out, together with the analysis of the results. A number of proposals for the optimization of low-force friction welding with the use of artificial intelligence have also been developed has also been developed. A simpler but less effective solution is application of neural networks. It is possible due to multiple digital recording and process automation parameters with digital recording and process automation This solution approach is not as productive as the proposed hybrid algorithm combining neural networks, fuzzy logic and genetic algorithmsThe hybrid method enables you to take advantages of all three algorithms in the position optimization.

Body Temperature and Heart Rate Monitoring System Using Fuzzy Classification Method

Climbing becomes one of the extreme sports that test endurance with nature, just like in a mountainous environment. In addition to the excitement and fun that climbing provides, climbers enjoy the opportunity to view breathtaking natural scenery and breathe in the fresh air drawn directly from the surrounding environment. Because of the temperature in the cold mountains, there are frequent and common obstacles Not realized by the climbers, such as hypothermia. Hypothermia is a condition in which the body temperature drops below 35oC. When body temperature is below normal 37oC, nervous system function and other body organs will experience interference. If not soon Left untreated, hypothermia can lead to heart failure, disturbances respiratory system, and even death. To anticipate things requires a system that functions to know the condition of mountaineer health. The system to be created uses the Mamdani fuzzy logic method, which decides whether the climber is healthy. The fuzzy logic method is used for decision-making based on body temperature and heart rate values. Implementation of the system in the form of a prototype containing sensors and mini-computers located at the climbing post, with data transmission using a node sent from post x to the main post to be uploaded to the database so that it can be known by the admin or rescue team when climbers need help in critical situations. This is done so that the condition can be monitored.

Prototipe Pendeteksi Kebakaran Menggunakan NodeMCU ESP8266 Dengan implementasi Logika Fuzzy

Fires doesn’t know the place and time, they can happen anywhere and anytime. Some of the causes of fires are human error, electrical short circuit, cigarettes, stoves, etc. As a result, many parties suffered losses, both property, business entities, and casualties. Based on these problems, we need a system that is able to detect fires and take preventive action, namely notifications via the internet using the Blynk application. In this study, the parameters used are the temperature sensor, the MQ-2 smoke sensor and the flame sensor. The output of the system is a buzzer, LED, LCD, and Blynk app as notifications. The Fuzzy Logic method is applied to the system to control the Buzzer, LED, LCD and notification determinants to the Blynk application.

Forecasting the environmental situation at the purification plants of the enterprise based on fuzzy logic

The study set the goal of improving the efficiency of various technological schemes of purification plants of the enterprise through the development of an expert system based on fuzzy logic methods and the formation of practical recommendations for improving the characteristics of wastewater from the operation of schemes of purification plants.

Multi-Criteria Optimization for Governmental Projects Priority Ranking Depending on Fuzzified Experts’ Opinion using Hygiene Approach

Each organization struggles to exploit each possible opportunity for gaining success and continuing with its work carrier. In this field, organization success can be concluded by fulfilling end user requirements combined with optimizing available resources usage within a specified time and acceptable quality level to gain maximum profit. The project ranking process is governed by the multi-criteria environment, which is more difficult for the governmental organization because other organizations' main target is maximizing profit constrained with available resources. The governmental organization should consider human, social, economic and many more factors. This paper focused on building a multi-criteria optimizing projects ranking framework using hygiene methodology from sequential stages. The proposed framework can deal with numerical and linguistic criteria considering experts, or consultant evaluation on projects, in addition to criteria weights. Fuzzy logic and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) methods are used to control the ranking process within a limited allocated budget. The importance of the proposed approach is making projects ranking getting out from dependence on economic measures that govern the overall process to non – measurable criteria that affect organization success and the proposed projects selection process.

Smart Room Lighting System for Energy Efficiency in Indoor Environment

The building sector absorbs 40% of global energy sources. Energy demand in the building sector is dominated by around 60 – 70% electricity, mainly used for air conditioning, water pumping machines, and lighting. On average, artificial lighting can consume 37% of the total electrical energy needs. Meanwhile, sunlight enters the room through the morning window from noon until the afternoon. Using unnecessary or excessive room lighting when there is a natural light source in the room consumes a relatively large total energy requirement of the building. There is a need for a smart lighting system specifically for indoors for efficient energy management and a lighting control system integrated with IoT, which utilizes the intensity of natural light in a room. In this paper, we proposed that the Smart Room Lighting System uses the fuzzy logic method based on ESP32 to control the lighting in the room to save electricity usage for a room lamp. The result of the tool's design, it can control the light starting from bright, dim, and lights go out. The results obtained by the Smart Room Lighting System can reduce power consumption by up to 93% and energy by up to 70%.

Identification of Potential Natural Aquifer Recharge Sites in Islamabad, Pakistan, by Integrating GIS and RS Techniques

Islamabad is essentially the only well-planned city in Pakistan, but groundwater depletion has become a serious issue there because of the rapid increase in population, poor water management, and deforestation. The current water demand of the city is about 220 million gallons per day, with the Capital Development Authority (CDA) providing up to 70 million gallons per day. The need for water is mostly fulfilled through groundwater sources, such as water bores and commercial tube wells. Hence, identifying recharge sites for natural aquifers is a significant component of groundwater required to overcome the water crisis. Therefore, this study aims to identify potential sites for natural aquifer recharge by using analytical hierarchy process (AHP), weighted linear combination (WLC), and fuzzy logic methods. To achieve the stated objective, seven local influencing factors including soil, slope, water table, population density, land use land cover (LULC), drainage density, and elevation have been utilized in this study. AHP was utilized for the evaluation of the relative importance of the above-mentioned factors, while fuzzy logic was applied for the standardization of these factors. Finally, the AHP-WLC and fuzzy logic approaches were used to merge factor maps in order to identify suitable sites for natural aquifer recharge in Islamabad City. Two different suitability maps were constructed from both techniques, and on each of the resulting maps, the subregions were categorized into five classes: not suitable, less suitable, moderate, suitable, and most suitable. Based on the AHP-WLC results, 5% of the whole study area is deemed most suitable for natural aquifer recharge (NAR), whereas from the fuzzy logic results, 10% of the study area is marked as most suitable. In contrast, 37% and 32% of the whole study area were identified as suitable by the AHP-WLC and fuzzy logic methods, respectively. While both techniques can obtain satisfactory outcomes, the suitability map from fuzzy logic has produced more precise results. Hence, we propose to CDA-Islamabad here different sites for recharge wells based on the results of fuzzy logic. As recommended by this study, to date CDA has constructed twelve recharge wells.

Mechanical peck drilling of Inconel 625: Parametric analysis and grey fuzzy logic based multi-response optimization

Nickel-based superalloy Inconel 625 is considered a hard-to-cut material due to characteristics such as high hardness, strain hardening behavior, and affinity with tool materials. It is challenging to machine through micro-holes of less than Ø 500 µm with the desired quality on Inconel 625 using the mechanical micro-drilling (MMD) process under dry conditions. The machining parameters must be selected optimally to enhance hole quality and to avoid frequent drill breakages. In this study, through micro-holes are machined on Inconel 625 by the MMD process using solid carbide micro-drills under dry conditions using a peck drilling strategy. Experiments are carried out by varying machining parameters such as spindle speed (8000 rpm, 11000 rpm, and 14000 rpm), feed (5 µm/rev, 7.5 µm/rev, and 10 µm/rev), and drill diameter (Ø 300 µm, Ø 400 µm, and Ø 500 µm) at three levels based on full factorial design. Thrust force and hole quality features such as exit burr height, radial overcut, and taper angle are measured as output responses. The mean effect plots are used to study the influence of machining parameters on output responses. The drill diameter had a significant effect on thrust force and radial overcut. Whereas feed and spindle speed had a major influence on exit burr height and taper angle, respectively. Finally, multi-response optimization is carried out using the grey fuzzy logic method, and an optimal machining parameter setting for multiple responses is ascertained.

Brain white matter hyperintensity lesion characterization in 3D T2 fluid-attenuated inversion recovery magnetic resonance images: Shape, texture, and their correlations with potential growth.

Analyses of age-related white matter hyperintensity (WMH) lesions manifested in T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have been mostly on understanding the size and location of the WMH lesions and rarely on the morphological characterization of the lesions. This work extends our prior analyses of the morphological characteristics and texture of WMH from 2D to 3D based on 3D T2 FLAIR images. 3D Zernike transformation was used to characterize WMH shape; a fuzzy logic method was used to characterize the lesion texture. We then clustered 3D WMH lesions into groups based on their 3D shape and texture features. A potential growth index (PGI) to assess dynamic changes in WMH lesions was developed based on the image texture features of the WMH lesion penumbra. WMH lesions with various sizes were segmented from brain images of 32 cognitively normal older adults. The WMH lesions were divided into two groups based on their size. Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH shape clusters (P = 1.57 × 10-3 for small lesions; P = 3.14 × 10-2 for large lesions). Significant differences in PGI were also found among WMH texture group clusters (P = 1.79 × 10-6). In conclusion, we presented a novel approach to characterize the morphology of 3D WMH lesions and explored the potential to assess the dynamic morphological changes of WMH lesions using PGI.

An improved mixed-integer programming approach for bi-objective parallel machine scheduling and location

This paper investigates a new bi-objective parallel machine scheduling and location problem: selecting locations for available machines, assigning jobs to these located machines for processing, and sequencing the assigned jobs on each machine to optimize the location cost and makespan, simultaneously. For the challenging NP-hard problem, we first develop a novel bi-objective mixed-integer linear programming (MILP) model with fewer integer variables compared with the state-of-the-art one. Then, several valid inequalities are proposed to tighten it further. We develop an ɛ-constraint based on the fuzzy-logic method to solve the bi-objective model. Computational results for benchmark instances show that the proposed approach obtains more Pareto-optimal solutions compared with the state-of-the-art one and is more than 15 times faster than it. Valid inequalities can reduce average computation time by more than 90%.

System Engineering of the Organizational and Technical Systems' Digital Twins Using Artificial Intelligence Methods

The article considers the problem of digital twin design of organizational and technical systems. The theoretical and methodological basis of the research is the fundamental scientific works and applied works of Russian and foreign scientists in the field of digitalization and digital twins. Following methods are used in the research: system analysis, statistical analysis, operational research, artificial intelligence. A comprehensive analysis of approaches and methods for digital twin design of organizational and technical systems is carried out. It is shown that for complex organizational and technical systems operating under uncertainty, there is no comprehensive, universal technology (methodological approach) for organization the process of developing digital twins in order to accelerate their engineering. The technology for organizing a digital twin design, combining design stages, methods and models, and providing its system engineering is proposed. The decision support model is developed for diagnosing the technical state of an object (technical device), based on the methods of situational analysis and fuzzy logic, which provides the synthesis of effective solutions under various situations and combinations of heterogeneous factors characterizing the object and its external environment. The practical significance of the research is the developed decision support system, which is the basis for control system in solving problems related to monitoring the current state of technical devices (instruments, equipment) and developing adequate decisions to eliminate their disfunctions.

Fuzzy Based Decision Support Model for Health Insurance Claim

Insurance industry in Indonesia has shown promising result based on premium growth in 2014-2018, as recorded in Indonesia General Insurance Market Update 2019. With the increase of premium, the claim rate also grows. Insurance companies face challenges in processing the claims. Many factors need to be carefully considered before making a claim decision. This paper proposes a decision support model (DSM) to score claim cases and to propose claim risk category (CRC) and claim decision (CD). The model was built with 13 parameters, divided into non-fuzzy group and fuzzy group. The analytic hierarchy process (AHP) method was used to determine the priority weight (PW) among parameters. The Tsukamoto’s fuzzy logic (FL) method was applied to process the fuzzy parameters. A simple mathematics method (SMM) was exercised to calculate the non-fuzzy parameters, and to aggregate the result into claim risk score (CRS). Finally, CRC and CD were derived from the CRS using a rule base. The model was tested using 19611 actual claim history records. The result was: 6171 (31.47%) accepted with CRC= low, 3459 (17.64%) pending (CRC medium), and 9981 (50.89%) pending (CRC high). The DSM model was implemented in python with Google COLAB and Datapane to create various graphics.

Fuzzy inference system (FIS) - long short-term memory (LSTM) network for electromyography (EMG) signal analysis

A wide range of application domains,s such as remote robotic control, rehabilitation, and remote surgery, require capturing neuromuscular activities. The reliability of the application is highly dependent on an ability to decode intentions accurately based on captured neuromuscular signals. Physiological signals such as Electromyography (EMG) and Electroencephalography (EEG) generated by neuromuscular activities contain intrinsic patterns for users’ particular actions. Such actions can generally be classified as motor states, such as Forward, Reverse, Hand-Grip, and Hand-Release. To classify these motor states truthfully, the signals must be captured and decoded correctly. This paper proposes a novel classification technique using a Fuzzy Inference System (FIS) and a Long Short-Term Memory (LSTM) network to classify the motor states based on EMG signals. Existing EMG signal classification techniques generally rely on features derived from data captured at a specific time instance. This typical approach does not consider the temporal correlation of the signal in the entire window. This paper proposes an LSTM with a Fuzzy Logic method to classify four major hand movements: forward, reverse, raise, and lower. Features associated with the pattern generated throughout the motor state movement were extracted by exploring published data within a given time window. The classification results can achieve a 91.3% accuracy for the 4-way action (Forward/Reverse/GripUp/RelDown) and 95.1% (Forward/Reverse Action) and 96.7% (GripUp/RelDown action) for 2-way actions. The proposed mechanism demonstrates high-level, human-interpretable results that can be employed in rehabilitation or medical-device industries.

Modelling of SO2 and NOx Emissions from Coal and Biomass Combustion in Air-Firing, Oxyfuel, iG-CLC, and CLOU Conditions by Fuzzy Logic Approach

Chemical looping combustion (CLC) is one of the most advanced technologies allowing for the reduction in CO2 emissions during the combustion of solid fuels. The modified method combines chemical looping with oxygen uncoupling (CLOU) and in situ gasification chemical looping combustion (iG-CLC). As a result, an innovative hybrid chemical looping combustion came into existence, making the above two technologies complementary. Since the complexity of the CLC is still not sufficiently recognized, the study of this process is of a practical significance. The paper describes the experiences in the modelling of complex geometry CLC equipment. The experimental facility consists of two reactors: an air reactor and a fuel reactor. The paper introduces the fuzzy logic (FL) method as an artificial intelligence (AI) approach for the prediction of SO2 and NOx (i.e., NO + NO2) emissions from coal and biomass combustion carried out in air-firing; oxyfuel; iG-CLC; and CLOU conditions. The developed model has been successfully validated on a 5 kWth research unit called the dual fluidized bed chemical looping combustion of solid fuels (DFB-CLC-SF).

Penerapan Metode Fuzzy Logic Pada Tempat Pemberi Pakan Kucing Menggunakan Sensor HC-SR04

This tool can help in feeding cats using the HC-SR04 Sensor. The author designed the application of the Fuzzy Logic Method to the Cat Feeder Using the HC-SR04 Sensor by making this tool useful as a feeder for cats. A tool is made that is expected to control or give food portions so that they do not experience a lack of food with a fairly good portion by applying the Fuzzy Logic method to control the distance for cats with the tool every day. If the feed provided has been used up by the cat, if the cat moves closer to the feed, the tool will automatically open. Automatic system by utilizing the HC-SR04 sensor, if the cat gets close to the feed it will automatically open with a range of 3 meters if it exceeds it will not open

Comparing Fuzzy Logic Mamdani and Naïve Bayes for Dental Disease Detection

Background: Dental disease detection is essential for the diagnosis of dental diseases. Objective: This research compares the Mamdani fuzzy logic and Naïve Bayes in detecting dental diseases. Methods: The first is to process data on dental disease symptoms and dental support tissues based on complaints of toothache consulted with experts at a community health centre (puskesmas). The second is to apply the Mamdani fuzzy logic and the Naïve Bayes to the proposed expert system. The third is to provide recommended decisions about dental diseases based on the symptom data inputted into the expert system. Patient data were collected at the North Cilacap puskesmas between July and December 2021. Results: The Mamdani fuzzy logic converts uncertain values into definite values, and the Naïve Bayes method classifies the type of dental disease by calculating the weight of patients’ answers. The methods were tested on 67 patients with dental disease complaints. The accuracy rate of the Mamdani fuzzy logic was 85.1%, and the Naïve Bayes method was 82.1%. Conclusion: The prediction accuracy was compared to the expert diagnoses to determine whether the Mamdani fuzzy logic method is better than the Naïve Bayes method. Keywords: Dental Disease, Expert System, Mamdani Fuzzy Logic, Naïve Bayes, Prediction

Mathematical and information models of decision support systems for explosion protection

This paper is dedicated to the issue of mathematical and information modeling of the combustion-to-explosion transition that makes it possible to create an adequate mathematical and information support for decision support systems (DSS) for automatedcontrol of explosive objects.A simple mathematical model for the transition of combustion to explosion is constructed. This model is based on solving mathematical problems of the hydrodynamic stability of flames and detonation waves. These problems are reduced to solving eigenvalue problems for linearized differential equations of gas dynamics. Mathematical model is universal enough. It provides opportunities for making simple analytical estimates for the explosive induction distance and the time of the shock wave formation. The possibilities of the transition of slow combustion to both a deflagration explosion and a detonation wave are considered. Theoretical estimates of the explosive induction distance and the time of the combustion-to-explosion transition are obtained. These estimates are expressed by algebraic a formula, the use of which save computer resources and does not require significant computer time. The application of fuzzy logic makes it possible to use the proposed mathematical model of the combustion-to-explosion transition for real potentially explosive objects in industry and transport.Mathematical models of potentially explosive objects are based on combination of the fuzzy logic and classical mathematical methods. These models give possibilities for creating corresponding information models. Thus mathematical and information support of DSS for automated control systems of explosive objects is developed. The main advantage of these DSS is that it makes it possible for decision makers to do without experts. In particular, developed mathematical and information models create the base forsoftware of DSS for explosion safety of grain elevators. Appropriate software is developed and some calculations are performed. These calculations are useful not only from the point of view of testing the proposed method of mathematical modeling of a grain elevator as a potentially explosive object or testing the software itself, but also from the point of view of the grain elevator designing.

Bayesian game model based unsupervised sentiment analysis of product reviews

Sentiment Analysis is a task of computationally recognizing and contextualizing opinions stated in a text. We mainly assess whether the writer's attitude towards a specific topic, or a product, is positive, negative, or neutral. Numerous machine learning and fuzzy logic methods have been reconnoitered for sentiment analysis. Yet, the application of mathematical optimization techniques for sentiment tagging is still unexplored. This study presents a novel mathematical framework for sentiment analysis of reviews based on Game Theory. We identify whether the sentiment of a review is positive or negative. In the first step, we comprehend a review and derive context scores from review comments using the SentiWordNet lexicon. We comprehensively combine the computed context and rating scores using the Bayesian Game Model to deduce the sentiment of reviews. Experimental results on three benchmark review datasets, viz. Food, Mobile, and Electronics demonstrate that the proposed model yields state-of-the-art results. We also statistically validated the stability and correctness of the results. The proposed model ensures rational and consistent results. The utility of the game theory model for sentiment analysis creates a new paradigm for diverse NLP tasks.

Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model

The precision of equivalent circuit model (ECM)-based state of charge (SoC) estimation methods is vulnerable to the variation of the battery parameters, due to several internal and external factors. In this regard, this study proposes a fuzzy logic method for the approximate estimation of the ECM parameters at different temperatures and SoC levels. The fuzzy inference system is designed to handle the non-linear deviation of the battery parameters from their reference values. On this basis, the extended Kalman filter and smooth variable structure filter are used to estimate the SoC. The two algorithms with fuzzy parameters (FP), namely FP-EKF and FP-SVSF, are tested on a 20 Ah Nickel Manganese Cobalt cell with maximum voltage of 4.2 V. The results show that the maximum root mean square error (RMSE) of the estimated SoC is kept within 1.51% with the FP-EKF and 0.68% with the FP-SVSF. Moreover, the reduction of the maximum absolute error may reach 0.34% with the FP-EKF, and 0.82% with the FP-SVSF, compared to the same algorithms without the proposed FP method. The executable codes are implemented on a low-cost controller, and the average computational time is obtained as 215 μs, which confirms the real-time practicality of the proposed method.

Covid-19 Virüsü Bulaş Riskini Etkileyen Parametrelerin Bulanık Mantık ile Değerlendirilmesi

As a result of the effects of the Covid-19 virus, which is thought to have emerged in China in December 2019 and caused the death of many people, 2020 has been declared a global pandemic year by the World Health Organization (WHO) as of March 2020. The distribution and consequences of the Covid-19 epidemic vary according to climates, regions, demographic structure, population, hygiene conditions and contamination. It has been proven by scientific research that the disease is transmitted from person to person through droplets and contact. Vaccine studies are continuing to prevent the Covid-19 epidemic. It is seen that it is not possible to return to normal life for a long time, and that people should follow all precautions with the same meticulousness even if they are vaccinated. Those who have to work without social distance in their workplaces and those who have to be together in public areas are at higher risk of contracting the Covid-19 virus. For this reason, the most important measures to prevent the spread of the virus in the community have been determined by the authorities as hygiene, maintaining social distance, staying in quarantine and wearing a mask. Accordingly, in this study, the effects of mask use, duration of stay in the closed environment and ventilation of the environment on the risk of transmission of the Covid-19 epidemic were investigated, and the relationships between these parameters were evaluated with the fuzzy logic method. Situations that can minimize the risk of Covid-19 virus transmission and maximize it have been identified.