Design and real time implementation of a novel rule compressed fuzzy logic method for the determination operating point in a photo voltaic system

Energy ◽  
2016 ◽  
Vol 116 ◽  
pp. 140-153 ◽  
Author(s):  
R. Rajesh ◽  
M. Carolin Mabel
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Operating Point ◽  
Fuzzy Logic Method ◽  
Photo Voltaic ◽  
Logic Method

scholarly journals Prototype of Control and Monitor System with Fuzzy Logic Method for Smart Greenhouse

2021 ◽  
Vol 3 (2) ◽  
pp. 116
Author(s):  
Didik Kurniawan ◽  
Arita Witanti
Keyword(s):  
Fuzzy Logic ◽  
Light Intensity ◽  
Real Time ◽  
Test Results ◽  
Water Pump ◽  
Moisture Sensor ◽  
Smart Systems ◽  
Level Sensor ◽  
Fuzzy Logic Method ◽  
Logic Method

To increase plant productivity, greenhouse buildings are needed that can protect plants from external factors and integrated with smart systems that can be monitored anytime and anywhere, and can provide optimal plant needs automatically. In this research, a system was built that can monitor greenhouse conditions in real time anywhere through Blynk application with IoT concept, as well as a system that can control output automatically with fuzzy logic method. The focus of control on this research is the duration of watering with Mini Water Pump and light intensity setting with LED Strip. This system is also equipped with FAN that can be active when the temperature is 31°C or more. Parameters used in this system are DHT22 sensor (air temperature and humidity), Soil Moisture sensor, Water Level sensor ,LDR sensor (light intensity) and RTC DS3231 (Real Time Clock), which is controlled with Arduino Mega 2560 microcontroller. In the test results obtained accuracy on fuzzy logic water pump by 93.1% and accuracy on fuzzy logic LED Strip by 99.6%. In the test results of the existing parameters, the results get a fairly optimal reading.

scholarly journals Egg Quality Detection System Using Fuzzy Logic Method

2019 ◽  
pp. 207-216
Author(s):  
Ikbar Mahesa ◽  
Aji Gautama Putrada ◽  
Maman Abdurohman
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Data Storage ◽  
Egg Quality ◽  
Detection System ◽  
Optimization Method ◽  
Light Sensor ◽  
Fuzzy Logic Method ◽  
Logic Method

Determining the quality of eggs in general is used by placing eggs on a flashlight. The detection system is very necessary to determine good egg quality or rotten eggs, so that the conditions of the eggs can be known by the chicken farm company and then will be sold to the community. This egg detecting system utilizes several sensor devices that are combined. The sensor used to detect the quality of eggs is a light sensor and a heavy sensor by connected with a microcontroller. So that there is no ambiguity towards the decision making of good egg or rotten eggs, then processing the data is obtained from these sensors using Fuzzy Logic and Firebase methods in real time as data storage media, and actuators will distribute or separate good eggs or the rotten eggs one. With the development of technology now, we can use the Internet of Things (IoT) technology, one of the systems check the quality of eggs which are good or not good. This system is built using a microcontroller to coordinate the running of the system using the Fuzzy Logic Method that applies inside. Final information is obtained on the form of egg quality in real time. The test results were carried out using the Fuzzy Logic method and obtained 95% results from 20 eggs and had 1 wrong egg. When using system hardware without using the fuzzy logic method on the microcontroller that using only a light sensor and a heavy sensor it produces a result of 75% from 20 eggs and had 5 wrong eggs. Using the egg detection optimization method can be increased up to 20%.

scholarly journals Using WSR-88D Polarimetric Data to Identify Bird-Contaminated Doppler Velocities

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Yuan Jiang ◽  
Qin Xu ◽  
Pengfei Zhang ◽  
Kang Nai ◽  
Liping Liu
Keyword(s):  
Fuzzy Logic ◽  
Real Data ◽  
Radar Data ◽  
Second Step ◽  
Doppler Velocity ◽  
Data Quality Control ◽  
Fuzzy Logic Method ◽  
Automated Technique ◽  
Migrating Birds ◽  
Logic Method

As an important part of Doppler velocity data quality control for radar data assimilation and other quantitative applications, an automated technique is developed to identify and remove contaminated velocities by birds, especially migrating birds. This technique builds upon the existing hydrometeor classification algorithm (HCA) for dual-polarimetric WSR-88D radars developed at the National Severe Storms Laboratory, and it performs two steps. In the first step, the fuzzy-logic method in the HCA is simplified and used to identify biological echoes (mainly from birds and insects). In the second step, another simple fuzzy logic method is developed to detect bird echoes among the biological echoes identified in the first step and thus remove bird-contaminated velocities. The membership functions used by the fuzzy logic method in the second step are extracted from normalized histograms of differential reflectivity and differential phase for birds and insects, respectively, while the normalized histograms are constructed by polarimetric data collected during the 2012 fall migrating season and sorted for bird and insects, respectively. The performance and effectiveness of the technique are demonstrated by real-data examples.

scholarly journals Feature Extraction in Six Blocks to Detect and Recognize English Numbers

2021 ◽  
pp. 3790-3803
Author(s):  
Heba Kh. Abbas ◽  
Haidar J. Mohamad
Keyword(s):  
Fuzzy Logic ◽  
Feature Extraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Suggested Method ◽  
Support Vector ◽  
Horizontal Line ◽  
Svm Algorithm ◽  
Fuzzy Logic Method ◽  
Logic Method

    The Fuzzy Logic method was implemented to detect and recognize English numbers in this paper. The extracted features within this method make the detection easy and accurate. These features depend on the crossing point of two vertical lines with one horizontal line to be used from the Fuzzy logic method, as shown by the Matlab code in this study. The font types are Times New Roman, Arial, Calabria, Arabic, and Andalus with different font sizes of 10, 16, 22, 28, 36, 42, 50 and 72. These numbers are isolated automatically with the designed algorithm, for which the code is also presented. The number’s image is tested with the Fuzzy algorithm depending on six-block properties only. Groups of regions (High, Medium, and Low) for each number showed unique behavior to recognize any number. Normalized Absolute Error (NAE) equation was used to evaluate the error percentage for the suggested algorithm. The lowest error was 0.001% compared with the real number. The data were checked by the support vector machine (SVM) algorithm to confirm the quality and the efficiency of the suggested method, where the matching was found to be 100% between the data of the suggested method and SVM. The six properties offer a new method to build a rule-based feature extraction technique in different applications and detect any text recognition with a low computational cost.

Sign in / Sign up

Export Citation Format

Share Document