scholarly journals Fuzzy Logic Application for Drought Risk Determination in Kulon Progo Regency, Daerah Istimewa Yogyakarta Province, Indonesia

2021 ◽  
Vol 2 (1) ◽  
pp. 68-81
Author(s):  
Bertolomeus Laksana Jayadri ◽  
Agus Maman Abadi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Risk Level ◽  
Drought Risk ◽  
Risk Map ◽  
Output Variable ◽  
Exposed Population ◽  
Northern Regions ◽  
Input Variables

This study aims to determine the drought risk of Kulon Progo Regency using fuzzy logic and study the characteristics. The input variables used in this study are the drought level, exposed population, and vulnerable population. The Mamdani method used in the fuzzy inference to obtain the output variable, that is, the Drought Risk Index (DRI). Then, the DRI are mapped to generate the drought risk map. The result shows that the fuzzy logic can be used to determine the drought risk. The drought risk level of the subdistricts in Kulon Progo Regency was fluctuated from 2010 to 2019. The drought risk level in 2010-2015 and 2019 were dominated by the low category. Meanwhile, the drought risk level in 2016-2018 was dominated by the very low category. Furthermore, the result also shows that the subdistricts located in the southern region of Kulon Progo Regency had a higher risk than those in the middle and northern regions during the last 10 years

The Application of a Fuzzy Approach to the Analysis of OSH Practitioners Level of Risk Acceptance

2014 ◽  
Author(s):  
Matilde A. Rodrigues ◽  
Celina P. Leão ◽  
Eusébio Nunes ◽  
Sérgio Sousa ◽  
Pedro Arezes
Keyword(s):  
Fuzzy Logic ◽  
Occupational Safety ◽  
Fuzzy Inference ◽  
Risk Level ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Risk Acceptance ◽  
Input Variables ◽  
Accident Scenarios ◽  
Level Of Risk

Organizations need constantly to take decisions about risk. In this process, Occupational Safety & Health (OSH) practitioners’ judgments have a great importance. If on one hand they have the technical knowledge about risk, on the other hand the decisions can be dependent on their level of risk acceptance. In view of this, this paper analyzes the views of the OSH practitioners about the level of risk acceptance, using the Fuzzy logic approach. A questionnaire to the analysis of the reported level of risk acceptance was developed and applied. The questionnaire included 79 risk scenarios, each accounted for the frequency of an accident with more lost workdays than a given magnitude. Through the two-step cluster analysis three groups of OSH practitioners were identified: Unacceptable, Tolerable and Realistic groups. A further analysis of the realistic group judgments about risk was performed, using the Fuzzy logic approach. The fuzzy sets of inputs and output variables were determined and the relationship between the variables was mapped through fuzzy rules. After that, the Min–Max fuzzy inference method was used. The obtained results show that the risk level is acceptable when input variables are at the lowest value and unacceptable when the risk level is high. Furthermore, the results obtained allow to better understand the uncertainty related with the OSH practitioners judgments being an important step to better understand the modeling of judgments about risk acceptance level allowing to know the different risk acceptance levels for the different accident scenarios.

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 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 A fuzzy system for cloacal temperature prediction of broiler chickens

2012 ◽  
Vol 42 (1) ◽  
pp. 166-171 ◽  
Author(s):  
Leandro Ferreira ◽  
Tadayuki Yanagi Junior ◽  
Wilian Soares Lacerda ◽  
Giovanni Francisco Rabelo
Keyword(s):  
Decision Support ◽  
Fuzzy System ◽  
Broiler Chickens ◽  
Broiler Chicken ◽  
Fuzzy Inference ◽  
Air Velocity ◽  
Output Variable ◽  
Average Standard Deviation ◽  
Inference System ◽  
Input Variables

Cloacal temperature (CT) of broiler chickens is an important parameter to classify its comfort status; therefore its prediction can be used as decision support to turn on acclimatization systems. The aim of this research was to develop and validate a system using the fuzzy set theory for CT prediction of broiler chickens. The fuzzy system was developed based on three input variables: air temperature (T), relative humidity (RH) and air velocity (V). The output variable was the CT. The fuzzy inference system was performed via Mamdani's method which consisted in 48 rules. The defuzzification was done using center of gravity method. The fuzzy system was developed using MAPLE® 8. Experimental results, used for validation, showed that the average standard deviation between simulated and measured values of CT was 0.13°C. The proposed fuzzy system was found to satisfactorily predict CT based on climatic variables. Thus, it could be used as a decision support system on broiler chicken growth.

scholarly journals DEVELOPMENT OF A SYSTEM FOR ASSESSING PASSENGER TRAFFIC BASED ON FUZZY LOGIC

2020 ◽  
Vol 4 ◽  
pp. 18-21
Author(s):  
Olha Pronina
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Public Transport ◽  
Fuzzy Inference ◽  
Technical Condition ◽  
Time Of Day ◽  
Output Variable ◽  
Passenger Traffic ◽  
Transportation Service ◽  
The City

A methodology has been developed for assessing public transport passenger traffic in the city. A mathematical model based on fuzzy logic is presented. The main criteria for assessing the attractiveness of passenger traffic are: the interval between vehicles, technical condition of the vehicle, route length, time of day. In the mathematical model, all input linguistic variables and output variable, their terms and membership functions are described. A fragment of a fuzzy knowledge base presented in the form of production rules is presented. At the exit, the dispatcher receives an output variable – the degree of confidence in the attractiveness of the route. Based on this assessment, the dispatcher can make a number of necessary changes to improve the functioning of the route. The software is implemented as a web service. This software will be convenient for dispatchers to use for planning public transport routes. Fifteen selected routes were taken for research, which are the most popular in the city. These routes were proposed for evaluation by three controllers. The results obtained from dispatchers were compared with the results of the fuzzy inference implemented in the software. The main advantage of using this software product is the ability to build a dynamic schedule based on the analysis of the dispatcher. This, in turn, will allow passengers to receive a better transportation service within the city

scholarly journals Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 103 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
Do-Hyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Significant Risk ◽  
Assessment Method ◽  
Logic Model ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Logic Model

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation

2016 ◽  
Vol 12 (1) ◽  
pp. 47-62 ◽  
Author(s):  
Asogbon Mojisola Grace ◽  
Samuel Oluwarotimi Williams
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Risk Level ◽  
Inference System ◽  
Wrong Decision

Credit risk evaluation techniques that aid effective decisions in credit lending are of great importance to the financial and banking industries. Such techniques assist credit managers to minimize the risks often associated with wrong decision making. Several techniques have been developed in the time past for credit risk evaluation and these techniques suffer from one form of limitation or the other. Recently, powerful soft computing tools have been proposed for problem solving among which are the neural networks and fuzzy logic. In this study, a neural network based on backpropagation learning algorithm and a fuzzy inference system based on Mamdani model were developed to evaluate the risk level of credit applicants. A comparative analysis of the performances of both systems was carried out and experimental results show that neural network with an overall prediction accuracy of 96.89% performed better than the fuzzy logic method with 94.44%. Finding from this study could provide useful information on how to improve the performance of existing credit risk evaluation systems.

scholarly journals Examining child obesity risk level using fuzzy inference system

2021 ◽  
Vol 10 (3) ◽  
pp. 679
Author(s):  
Febrina Sari ◽  
Desyanti Desyanti ◽  
Teuku Radillah ◽  
Siti Nurjannah ◽  
Julimar Julimar ◽  
...  
Keyword(s):  
Childhood Obesity ◽  
Fuzzy Inference System ◽  
Fast Food ◽  
Fuzzy Inference ◽  
Risk Level ◽  
Food History ◽  
Inference System ◽  
Input Variables ◽  
Level Of Risk ◽  
Obesity In Children

The doctor will determine the risk level of childhood obesity by using standard calculations, namely measuring the child's weight and height, and many other factors. Then the doctor will calculate the child's body mass index (BMI). The results of calculations made by the doctor will be compared with standard/normal values set by FAO/WHO, to obtain the level of risk of obesity in children. This study aims to analyze the risk level of obesity in children using the Sugeno method of Fuzzy Inference system, using the trapezoidal membership function and involving six input variables such as exercise habits, consumption of fast food, history of obesity of parents, and others. The application of the fuzzy inference system Sugeno method can help doctors to analyze the risk level of childhood obesity quickly and accurately with an accuracy rate of 85%. The results of the implementation of the Sugeno method of Fuzzy Inference system showed that out of 140 children who were the object of the study, 119 children received a diagnosis of the level of risk of obesity which was the same as the diagnosis made by a doctor.

scholarly journals PENERAPAN LOGIKA FUZZY DALAM OPTIMASI PRODUKSI BARANG MENGGUNAKAN METODE MAMDANI

2017 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Wahyu Toto Priyo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Set ◽  
Alternative Solution ◽  
Output Variable ◽  
Centroid Method ◽  
Production Problem ◽  
Input Variables ◽  
Production Problems ◽  
Maximum Method

Developement and revolution of the era are always followed by the development of technology and sciences. Along with the development of the era, it will be found various problems in various aspects of life, including in the field of industry. Often companies experience obstacles in meeting the number of requests from consumers, or even the amount of inventory of many goods. From these problems required an appropriate solution for production problems can be resolved. In this research, we will apply Fuzzy Logic as one of alternative solution of goods production problem, that is by using Mamdani method, with the quantity of demand and inventory of goods as input variable and quantity of goods produced as output variable. Then followed by 4 stages, namely: (a) Formation of fuzzy set by fuzzification process, (b) Application of implication function, (c) Composition of rules with maximum method, (d) Process defuzzification with centroid method that will result outout amount of goods which must be produced by the company. From the results of the analysis that has been done, by entering the input variables the number of requests amounted to 54,900 units and the amount of inventory amounted to 4060 units produce output production amounted to 46,600 units.Keywords: Fuzzy Logic, Mamdani methode, goods production

scholarly journals Fuzzy Evaluation of Crowd Safety Based on Pedestrians’ Number and Distribution Entropy

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 832
Author(s):  
Xuguang Zhang ◽  
Qinan Yu ◽  
Yuxi Wang
Keyword(s):  
Public Management ◽  
Fuzzy Inference ◽  
Video Monitoring ◽  
Video Sequences ◽  
Fuzzy Evaluation ◽  
Output Variable ◽  
Inference System ◽  
Small Areas ◽  
Distribution Uniformity ◽  
Input Variables

Crowd video monitoring and analysis is a hot topic in computer vision and public management. The pre-evaluation of crowd safety is beneficial to the prediction of crowd status to avoid the occurrence of catastrophic events. This paper proposes a method to evaluate crowd safety based on fuzzy inference. Pedestrian’s number and distribution uniformity are considered in a fuzzy inference system as two kinds of attributes of a crowd. Firstly, the pedestrian’s number is estimated by the number of foreground pixels. Then, the distribution uniformity of a crowd is calculated using distribution entropy by dividing the monitoring scene into several small areas. Furthermore, through the fuzzy operation, the fuzzy system is constructed by using two input variables (pedestrian’s number and distribution entropy) and one output variable (crowd safety status). Finally, inference rules between the crowd safety state and the pedestrian’s number and distribution uniformity are constructed to obtain the pre-evaluation of the safety state of the crowd. Three video sequences extracted from different scenes are used in the experiment. Experimental results show that the proposed method can be used to evaluate the safety status of the crowd in a monitoring scene.

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