scholarly journals The Design of a Portable Municipal Waste Incinerator With Fuzzy Logic Based Support for Emission Estimation

2016 ◽  
Vol 5 (3) ◽  
Author(s):  
Jude C. Akpe ◽  
Olatunde A. Oyelaran ◽  
Ibrahim O. Abdulmalik
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Low Carbon Steel ◽  
Maximum Temperature ◽  
Waste Incinerator ◽  
Low Carbon ◽  
Possibility Distribution ◽  
Inference System ◽  
Complete Combustion ◽  
Input Variables

A fuzzy logic interface system to estimate oxygen requirement for complete combustion as well as the level of pollution from incinerator gas flue in order to manage solid waste from domestic, institutional, medical and industrial sources was designed. The designed incinerator is double chambered operating with a maximum temperature of 760 °C in the lower chamber and 1000°C in the upper chamber.  The insulating wall is made up of a refractory brick of 55mm in thickness having a 2mm thickness low carbon steel as the outer wall.  Hydrogen Chloride (HCl) and Nitrous oxides (NO)x are the gases was used to demonstrate the Fuzzy Inference System (FIS) model. The FIS was built with five input variables (Food, PVC, Polythene, Paper and Textile) and three input variables with two membership functions. The FIS was developed to estimation the degree of possibility distribution of pollution that should be expected when a certain composition of waste is incinerated. The plots of composition of waste high in food against oxygen require for combustion gives a possibility distribution of about 0.9 which is high according to the fuzzy set definition while the plot of waste composition high in PVC against HCL shows linearity.

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

scholarly journals Determination of Difficulty Level for Garment Model with Fuzzy Logic Method

2021 ◽  
Vol 28 (121) ◽  
pp. 39-47
Author(s):  
Hilal Bilgiç ◽  
Yusuf Kuvvetli ◽  
Pınar Duru Baykal
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Real Life ◽  
Operation Time ◽  
Difficulty Level ◽  
Decision Framework ◽  
Inference System ◽  
Fuzzy Logic Approach ◽  
Input Variables ◽  
Fabric Weight

The purpose of this study is a rule-based fuzzy logic approach is proposed for determining model difficulty in manufacturing top clothing for ladies. A decision framework concerned with different scenarios (main pattern types and material types) is proposed for determining the model difficulty. Each scenario modeled as a Mamdani type fuzzy inference system which is known as one of the best approximator fuzzy logic models. The fuzzified input variables are unit operation time, second quality rate and fabric weight. Moreover, two different defuzzification methods which are centroid and middle of maxima are compared for finding best fuzzy logic structure over the six different test instances. According to the results, both deffuzzification methods find similar model difficulty determinations. A graphical user interface of the proposed decision framework is designed in order to apply this to real-life applications. Finally, six different clothing models are identified to be simple, medium-hard, hard and very hard. The results of this study showed that defuzzification methods is not significantly effected the model difficulty decisions off is systems regarding different test instances. The model difficulty values range between 0-10. In order to find a useful difficulty assignment (linguistic), the model difficulty is determined by using the closeness to center value (a2) of membership functions. This research offers a solution to determine the difficulty levels of the garment models.

scholarly journals PENILAIAN MUTU DOSEN TERHADAP TRI DHARMA PERGURUAN TINGGI DENGAN MENERAPKAN LOGICA FUZZY LOGIC DI STIKES NAN TONGGA

2019 ◽  
Vol 8 (2) ◽  
pp. 175
Author(s):  
Tri Monarita Johan ◽  
Renty Ahmalia
Keyword(s):  
Higher Education ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Teaching Staff ◽  
Output Variable ◽  
Inference System ◽  
Quality Value ◽  
Input Variables ◽  
Collection Methods ◽  
Functional Position

Tri Dharma of Higher Education is an activity that must be carried out by every Lecturer. In this study an application was designed to apply Fuzzy logic to calculate the quality value of Lecturers on the implementation of Higher Education Tri Dharma. Higher Education has the aim of producing quality qualifications. Therefore we need competent teaching staff needed. The background of this research is to study the results obtained from the application and calculation using Fuzzy logic, also help the lecturer evaluation in the field of quality control. The Mamdani Method is often also known as the Max-Min Method. This method was introduced by Ebrahim Mamdani in 1975. To get results, four stages are needed: 1. The formation of the fuzzy set; 2. Application function implications (rules); 3. Composition of rules; 4. Affirmation (deffuzy). The results obtained in this study the value of the function that has been optimized where lecturers will get the best in performance. Data collection methods in the fuzzy inference system function meeting, the author requires input data consisting of three variables and one output variable. Input variables consist of: 1. Research Variables 2. Dedication Variables 3. Teaching Variables. 4. Functional Position Variables After calculations and experiments, the results obtained using the Fuzzy Mamdani method with Matlab

scholarly journals Line Follower Robot Optimization based Fuzzy Logic Controller Using Membership Function Tuning

2018 ◽  
Vol 7 (2.2) ◽  
pp. 112
Author(s):  
Supriadi Supriadi ◽  
Ansar Rizal ◽  
Didi Susilo Budi Utomo ◽  
Agusma Wajiansyah
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Fuzzy Inference ◽  
Inference System ◽  
On Line ◽  
Input Variables ◽  
The Right ◽  
Triangular Membership Function

The study was aimed to measure the performance of Fuzzy Logic Controller (FLC) on Line Follower Robot (LFR). FLC output is a deviation value of Pulse Width Modulation (PWM) to determine the rotational speed of the left and the right wheel. As input variables are current and previous line sensors. Tuning was applied to input and output variables in each membership function (MF) to conduct the best performance. This study used triangular membership function that consists of three MF. Mamdani Fuzzy Inference System (FIS) is used using nine rules. The result obtains that after MF tuning, the performance of the LFR settling time is 0.63s faster compare to that without tuning.  

scholarly journals A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Arati M. Dixit ◽  
Harpreet Singh
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Source Identification ◽  
Field Programmable Gate Array ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Field Programmable ◽  
Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

scholarly journals Anfis to Detect Brain Tumor Using MRI

2018 ◽  
Vol 7 (3.27) ◽  
pp. 209
Author(s):  
Susmita Mishra ◽  
M Prakash ◽  
A Hafsa ◽  
G Anchana
Keyword(s):  
Fuzzy Logic ◽  
Brain Tumor ◽  
Graphical Representation ◽  
Fuzzy Inference ◽  
Early Stage ◽  
Histogram Equalization ◽  
Input Image ◽  
Inference System ◽  
Magnetic Resonance Imaging Mri ◽  
Contrast Stretching

Processing of Magnetic Resonance Imaging(MRI) is one of the widely known best techniques to diagnose brain tumor since it gives better results than ultrasound or X-Ray images. The main objective is to diagnose the presence and extraction of brain tumor using MRI images. Image preprocessing includes contrast stretching, noise filtering and Adaptive Histogram Equalization(AHE). AHE gives a graphical representation of digital image without enhancing above the desired level. The next stage involves transferring the redundant information in input image to reduced set of features is called feature selection and is done by color, shape or texture of an image. Image is segmented using incorporation of Artificial Neural Networks(ANN) and Fuzzy logic called Adaptive Neuro-Fuzzy Inference System(ANFIS) wherein we get the desired output to differentiate tumor affected and normal image with its severity level. Since we deal with uncertainty much more, fuzzy logic serves as a vibrant tool in representing human knowledge as IF-THEN rules. MATLAB has been implemented in detection and extraction of tumor at an early stage. 

scholarly journals Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

scholarly journals Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Venny Riana Riana Agustin ◽  
Wahyu Henky Irawan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Fuzzy Rules ◽  
Quality Of Services ◽  
Inference System ◽  
A Value ◽  
Fuzzy Logic Analysis

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

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