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The control device of a vehicle's anti-lock braking system (ABS) most commonly implements two-position control. In modern automation systems, many other control laws are used, which in certain cases are much better than the two-position control. This article discusses the development of a fuzzy PID controller for the control of an ABS. The fuzzy PID controller is tuned using the optimization method, which employs a genetic algorithm to determine the optimal solution. A comparison is made between the transient processes in the ABS control device with a two-position controller, a PID controller and a fuzzy PID controller, and the robustness of the system is evaluated. The 14 s braking time for the two-position controller decreases to 13 s when operating with a PID controller and to 11 s when operating with a fuzzy controller, which represents a 25% reduction in braking time, and therefore a shorter braking distance. The developed fuzzy controller also provides a better system robustness and will reduce pressure fluctuations in the pipes. [ABSTRACT FROM AUTHOR]

Carbon dioxide or CO2 emissions are the main component of Greenhouse Gases that can increase the Greenhouse Effect. The enhancement of CO2 concentration will cause more heat waves to be reflected from the surface of earth and then the atmosphere absorbed it. As the result, the average temperature of the earth increases or a global warning. Based on the description above, it is necessary to predict the amount of CO2 emissions produced by vehicles which in this study are cars. So that it can be predicted the magnitude of the environmental burden caused by these transportation activities. In calculating the contribution of CO2 generated by the car, it is done by applying the Mamdani Fuzzy Inference system with Centroid method. From the study conducted, the Mamdani fuzzy inference system model with the Centroid method in predicting carbon dioxide emissions from cars obtained an accuracy of 0.901 for the r square score and 18.22 for Root Mean Square Error. [ABSTRACT FROM AUTHOR]

The electronics laboratory is a place for practicum for electronics students. In this laboratory there are electronic components that must be protected from temperature and humidity. If the temperature is too cold and the humidity is too high, it can cause corrosion of electronic components, which can cause damage. Arduino is an open source electronic kit specially designed to make it easier for everyone to develop electronic devices that can interact with various sensors and controllers. Laboratory room temperature detection system using software and hardware. The software used is Arduino and the MySql database, while the hardware is power supply, D1 Mini microcontroller, DHT11 sensor and LCD. The temperature data obtained from the DHT11 sensor will be displayed on the LCD. In data processing using fuzzy method. [ABSTRACT FROM AUTHOR]

One of the potential renewable energy sources in Indonesia is solar energy. Indonesia, which is passed by the equator, has abundant sources of solar energy with an average solar radiation intensity of around 4.5 kWh/m2 per day throughout the region. In addition to the high cost of components and installation, the efficiency of solar cells is still low. Increasing the temperature of the solar panels due to continuous heating can reduce the efficiency of the panels. The decrease in solar cell efficiency is about 0.5% for an increase in the surface temperature of solar cells by 1o C. Therefore, the addition of a proper cooling system in solar power plants can also improve efficiency and increase the output power of solar panels. This research focuses on increasing the efficiency of solar power generation systems with fuzzy logic. The input system has an LDR sensor to move the solar panel to follow the direction of the sun, a temperature sensor as an input to control the temperature of the solar panel. Sequence of research methods, literature study, preparing tools and materials, design, testing, and data processing. This research found that solar panels with a solar tracker and cooling based on fuzzy logic obtained an efficiency percentage of 15.51% compared to static solar panels. [ABSTRACT FROM AUTHOR]

Rice fields have a strategic function because they are the main food providers for the population in Indonesia. The conversion of rice fields tends to experience an increase that makes the emergence of new problems because agricultural land will be replaced with the housing sector or factories. From the thing, Therefore, it is necessary to make modern rice fields means that the planting and harvesting process does not have to be done in a large rice field. As for the components used such as the microcontroller wemos D1 R1, groundhog sensor, DHT sensor, RTC sensor, fan and pump, and also the results of this research. Obtained from the test results that are declared successful, in the form of the implementation of fuzzy logic with rule-based on microcontrollers with soil moisture and DHT sensor inputs, and as a fan and pump output and with fuzzy sets included in the form of fast, medium, old, and fertilization scheduling with RTC sensor. With the addition of technology, it is expected that modern farmers will get more effective in carrying out a plant monitoring and care system. [ABSTRACT FROM AUTHOR]

The purpose of the research is to detect defects in textile industry using computer vision and fuzzy logic techniques. The paper presents various practices used for edge detection, including a fuzzy logic applied. By testing, a fuzzy logic method has been chosen to obtain a respectable result. The algorithm that is observed in the paper is improved by dividing the processing into two phases: a fast one, which allows us to assume that there is a defect, and a searching one, which allows a defect to be highlighted in color. The advanced algorithm was tested on several types of fabrics. An average background arrangement is obtained for them. That allows separating fabric texture and color from potential defects. The tests were also performed on fabrics (cloths) with different brightness, number and type of defects. We obtained the percentage ratios of the defect detection quality and so we suggested the ways to develop the system. [ABSTRACT FROM AUTHOR]

Production processes of materials heat treatment are characterized by a large number of interrelated control parameters, inertia, non-stationarity and significant nonlinearity. That is why they can be described only using models in the form of a verbal description in natural language, which allow the unique experience of experts in the subject area of process control to be translated into the language of correct control computer programs. Traditionally, fuzzy logic controllers are considered an automation tool for these objects. However, when using them to control heat treatment processes, which, as a rule, are multidimensional, it turned out that due to the large error and low speed, significant difficulties arise in the synthesis of a compensator for the mutual influence of the loops of a multidimensional fuzzy logic controller. These disadvantages are deprived multidimensional logic controllers (MLC), the input and output variables of which are interpreted by a set of precise terms. The error of such controllers in comparison with fuzzy ones is lower, and the speed is higher. In addition, the capabilities of the MPLC for controlling poorly formalized multi-connected production processes are almost the same as those of fuzzy controllers. [ABSTRACT FROM AUTHOR]

White Feces Disease is caused by the dominance of vibrio bacteria in the digestive organs. WFD causes shrimp feces to be elongated and whitish. The WFD detection process requires L.vaname shrimp samples to be taken for hemocyte cells. Detection of WFD by looking at the characteristics of the shrimp, such as the presence of white feces in the water, indicates that the research results are inaccurate because it is not always certain that it is feces from infected shrimp. WFD detection can be done using fuzzy calculations on the Total Hemocyte Count of L.vaname shrimp to obtain the number of vibrio bacteria. The method used in the detection is the COA method which is one of the defuzzification methods to obtain a solution by taking the center point of the fuzzy area. This method can make WFD detection results in L.vaname shrimp more effective and accurate. This study aims to detect the presence of White Feces Disease (WFD) in l.vaname shrimp using the fuzzy method and to find out how to apply the website-based White Feces Disease (WFD) detection to l.vaname shrimp. From fuzzy logic calculations, it was found that the number of vibrio bacteria was 12.5 x 10⁴ CFU/ml, so it can be concluded that the l.vaname shrimp that were studied were infected with WFD. and it can also be concluded that the application of White Feces Disease (WFD) detection to the L.vaname shrimp was based on the website. [ABSTRACT FROM AUTHOR]

Micro Hydro Power Plant (MHPP) is a type of renewable energy plant that has the potential to continue to be developed in Indonesia. In operation, the MHPP will generally have a more stable output power when using an Electronic Load Controller (ELC) compared to the Power System Stabilizer (PSS) and Governor Control. ELC can produce a faster response in stabilizing the frequency due to load changes. However, it results in quite high harmonics in the output power, which can cause damage to electronic loads. The aim of this study is to develop an ELC with lower harmonics evenly at the output power when the consumer load fluctuates. The harmonic level is suppressed by adding an active harmonic filter (AHF) to the ELC. An Artificial Neural Fuzzy Inference System (ANFIS) control system was added to improve AHF performance to make it more optimal. The proposed control scheme is simulated using MATLAB Simulink. The simulation results show that the AHF can function properly in suppressing harmonics when there is a load change. Then, the presence of ANFIS in the control scheme can suppress harmonics even better. The combination of AHF and ANFIS on the ELC was able to improve the quality of the MHPP output power. [ABSTRACT FROM AUTHOR]

The problem with two-wheeled vehicles is that this system is unbalanced, especially at low speed or in stationary states. A gyroscopic precession from a Control Moment Gyroscope (CMG) is used to balance on a two-wheeled vehicle prototype. A CMG consists of a spinning flywheel and two motorized gimbals that tilt the flywheel's angular momentum. As the gimbal rotates, the changing angular momentum causes a gyroscopic torque that is used as balancing torque. This study aims to implement fuzzy logic to control the gimbal rotation on the control moment gyroscope. Two functions of Sugeno-type fuzzy logic control are used: the triangle function with the trapezoid function. The experimental results show that the triangular function performs better than the trapezoidal function. [ABSTRACT FROM AUTHOR]

This document discusses solar-based DC power supply systems. Algorithms for finding extreme power with a fixed step have been developed. Diagrams of the current and voltage signals of the solar battery of the controller in the extreme power search mode were built. Also, diagrams of the change in the current and voltage of the SS are built during the operation of the algorithm for finding the extreme power of the SS controller with variable pitch. Based on the obtained values, conclusions were drawn. [ABSTRACT FROM AUTHOR]

Fuzzy logic is a logical system that basically provides the basis for approximate reasoning using imprecise solutions and allows the use of linguistic variables. The purpose of the work is to study the influence of the use of membership functions of various forms on the results of fuzzy logical inference. In the course of the work, the temperature control of the outgoing steam will be used as an adjustable parameter, and the influence of the membership functions on the output results will also be studied. The stabilization of the steam temperature in individual sections and at the outlet of the superheater path ensures the maintenance of minimum steam humidity, increases the safety of metal work, as well as the efficiency of the boiler plant. [ABSTRACT FROM AUTHOR]

In the past 30 years, switched reluctance motors (SRMs) have attracted a lot of interest in the field of variable speeding drives, according to this review study. It could be a viable substitute for other traditional AC apparatus that are often utilized. due to two key factors: The first reason is the rapid advancement in power electronic apparatus at this period, and the second is the special qualities of this motor, including its straightforward designing, robustness, great efficiency, and simplicity and speeding of speeding control. While this motor has many advantages, it also has drawbacks, including the fact that the torque it produces varies over time. The aim of this study is to contrast the traditional controllers efficiency depending on P&PI and fuzzy logic controllers depending on artificial intelligence (AI) to emphasize the efficiency of the efficient controllers. The torque has a ripple that reduces the motor's efficiency. [ABSTRACT FROM AUTHOR]

One of the main forces advancing Industry 4.0, also known as the fourth industrial revolution, is machine learning (ML). This paper examines the machine learning (ML) models application for biogas production prediction in anaerobic digestion (AD). This study's primary objective is to determine which ML techniques and models are utilised in the AD process. In addition, this study identifies the type of ML techniques, input and output parameters, and software used. Researchers have widely employed a couple of ML models in biogas production. After reviewing the 15 most recent papers, it was discovered that the Artificial Neural Network (ANN) and Adaptive Network-Based Fuzzy Inference System (ANFIS) are the most commonly used types of ML. The most commonly used operating parameters in predicting biogas production were reaction time, temperature, pH, total solids (TS), volatile solids, volatile fatty acids (VFAs), and fixed solids. In conclusion, the review discusses the challenges and prospects of using machine learning in the AD process and provides recommendations for future implementation. [ABSTRACT FROM AUTHOR]