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.

scholarly journals Analisis Performance Fuzzy Tsukamoto Dalam Klasifikasi Bantuan Kemiskinan

Tech-E ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 49
Author(s):  
Sri Redjeki
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Original Data ◽  
Poverty Rate ◽  
Poor People ◽  
Membership Functions ◽  
Test Results ◽  
Inference System ◽  
Poverty Relief ◽  
Correct Data

The Central Bureau of Statistics (BPS) showed that the poverty rate in Indonesia in September 2014 still high at about 27.7 million people, or about 10.96%. As a basis for policy countermeasures, understand the problem of poverty often demands the effort of defining, measuring, and identifying the root causes of poverty. This study wanted to use one of the methods that exist in fuzzy logic to classify beneficiaries of poverty that exist in Bantul. Fuzzy Inference System used in this study using Tsukamoto with 8 rule established by a group of poor criteria and types of poverty relief. There are three groups of criteria of poverty derived from 11 criteria of poverty in Bantul. While the types of assistance that are used are Raskin, BLT and KUR. The system is built using PHP. To see the performance Tsukamoto method in this study used 50 data poor people in Sub Districs Banguntapan. From the test results turned out to obtained an accuracy of 52%, meaning that there were 26 correct data according to the original data. It is necessary to modify the rules and membership functions to improve system accuracy results

Development of a Fuzzy Logic-Based Model for Monitoring Cardiovascular Risk

2019 ◽  
pp. 172-190
Author(s):  
Peter Adebayo Idowu ◽  
Sarumi Olusegun Ajibola ◽  
Jeremiah Ademola Balogun ◽  
Oluwadare Ogunlade
Keyword(s):  
Heart Failure ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Vital Signs ◽  
Healthcare Services ◽  
Logic Model ◽  
Risk Indicators ◽  
Membership Functions ◽  
Input And Output

Cardiovascular diseases (CVD) are top killers with heart failure as one of the most leading cause of death in both developed and developing countries. In Nigeria, the inability to consistently monitor the vital signs of patients has led to the hospitalization and untimely death of many as a result of heart failure. Fuzzy logic models have found relevance in healthcare services due to their ability to measure vagueness associated with uncertainty management in intelligent systems. This study aims to develop a fuzzy logic model for monitoring heart failure risk using risk indicators assessed from patients. Following interview with expert cardiologists, the different stages of heart failure was identified alongside their respective indicators. Triangular membership functions were used to fuzzify the input and output variables while the fuzzy inference engine was developed using rules elicited from cardiologists. The model was simulated using the MATLAB® Fuzzy Logic Toolbox.

Development of a Fuzzy Logic-based Model for Monitoring Cardiovascular Risk

2015 ◽  
Vol 10 (4) ◽  
pp. 38-55
Author(s):  
Peter Adebayo Idowu ◽  
Sarumi Olusegun Ajibola ◽  
Jeremiah Ademola Balogun ◽  
Oluwadare Ogunlade
Keyword(s):  
Heart Failure ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Vital Signs ◽  
Healthcare Services ◽  
Logic Model ◽  
Risk Indicators ◽  
Membership Functions ◽  
Input And Output

Cardiovascular diseases (CVD) are top killers with heart failure as one of the most leading cause of death in both developed and developing countries. In Nigeria, the inability to consistently monitor the vital signs of patients has led to the hospitalization and untimely death of many as a result of heart failure. Fuzzy logic models have found relevance in healthcare services due to their ability to measure vagueness associated with uncertainty management in intelligent systems. This study aims to develop a fuzzy logic model for monitoring heart failure risk using risk indicators assessed from patients. Following interview with expert cardiologists, the different stages of heart failure was identified alongside their respective indicators. Triangular membership functions were used to fuzzify the input and output variables while the fuzzy inference engine was developed using rules elicited from cardiologists. The model was simulated using the MATLAB® Fuzzy Logic Toolbox.

scholarly journals Development of pre-time intersection isolated signal using a fuzzy logic model

2019 ◽  
Vol 270 ◽  
pp. 03002
Author(s):  
Moch D. Studyana ◽  
Ade Sjafruddin ◽  
Iwan P. Kusumantoro ◽  
Yudi Soeharyadi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Final Analysis ◽  
Logic Model ◽  
Time Signal ◽  
Large Cities ◽  
Performance Improvements ◽  
Research Survey ◽  
Fuzzy Logic Model ◽  
Model Study

We investigate the development of pre-time signal intersection operating systems for isolated intersections using Fuzzy Logic models. The traffic signal system setting in Indonesia has been using the Indonesia Road Capacity Manual model 1997, for example it is installed at the intersection in large cities in Indonesia. The development of the Fuzzy Logic model is focused on improving the performance of the signaled intersection, using a combination of numerical variable analysis used by IRCM 1997, and the linguistic or traffic behavior variable as the basis of the Fuzzy Logic model study. The combination of the two variables in the Fuzzy Logic model analysis is expected to improve the intersection performance. The Fuzzy Logic model process involves the Membership Function theory as the basis for the confidence level of the traffic variable to be surveyed, and the Fuzzy Inference Engine to measure the choice of combinations of variables that will be selected to make the best performance of the intersection. The geometric of intersection must be control as it involves the input of research data, especially on the condition of the intersection legs and markers of motor cycle-special stopping places, which is a potential of a traffic violation by traveller. The model is verified with fuzzified data from 2017 traffic research survey in Bandung. As an illustration of the majority of intersection setting with an isolated pre-time operating system, there are 60 intersection points or 85% of the total 71 intersections available. This would be a potentially major problem when performance improvements is not carried out. The final analysis shows that the number of vehicles queues decreases while the traffic flows passing through the intersection increases, therefore fuzzy logic model is expected to contribute and to give alternative handling for intersection performance with pre-time operational.

ANFIS for Predicting Surface Roughness in Turning Operation Performed on CNC Lathe

2011 ◽  
Vol 110-116 ◽  
pp. 1793-1798
Author(s):  
M.A. Vinod Kumar
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Cutting Parameters ◽  
Membership Functions ◽  
Ic Engine ◽  
Inference System ◽  
Cnc Lathe ◽  
Testing Data ◽  
Inference Systems

For mass production, mainly automation is used, in which cutting parameters are set to obtain required surface roughness. The parts like IC Engine piston, cylinders require very smooth surface finish. The same is the case of sleeves, collets etc., of machine parts. These are made by automatic machining operations. To get approximate value of required surface roughness, the cutting parameters that are to be set with help of Adaptive Neuro Fuzzy Inference System (ANFIS) that is designed by using Fuzzy Logic Toolbox. The Fuzzy Logic Toolbox is a collection of functions built on the MATLAB numeric computing environment. It provides tools to create and edit fuzzy inference systems (FIS) within the framework of MATLAB. ANFIS constructs a relation between given parameters (input data and output data), when it is trained with experimentally predetermined values. It consists of different functions, of which bell and triangular membership functions are used for our purpose. The comparison of accuracy of predicted values for both membership functions are performed using testing data. The training and testing data was obtained performing operation on CNC lathe for 50 work pieces of which 40 were used for training ANFIS and the remaining 10 were used for comparing the accuracy of both Bell and Triangular membership functions. The detailed analysis and procedure is presented.

Prediction of Risk of Wet-Pavement Accidents: Fuzzy Logic Model

2000 ◽  
Vol 1717 (1) ◽  
pp. 28-36 ◽  
Author(s):  
Jie Xiao ◽  
Bohdan T. Kulakowski ◽  
Moustafa EI-Gindy
Keyword(s):  
Fuzzy Logic ◽  
Nonlinear Regression ◽  
Probabilistic Models ◽  
Fuzzy Inference ◽  
Logic Model ◽  
Nonlinear Regression Models ◽  
Fuzzy Logic Model ◽  
Logic Models ◽  
Fuzzy Clustering Method ◽  
Accident Data

Researchers developed a fuzzy-logic model for predicting the risk of accidents that occur on wet pavements. Preventing wet-pavement accidents has been an extremely difficult and elusive task because they are stochastic events whose occurrence is related to a variety of factors, including vehicle, roadway, human, and environmental characteristics. Conventionally, researchers use linear or nonlinear regression models and probabilistic models to predict wet-pavement accidents. However, these models often are limited in their capability to fully explain the process when the underlying physical system possesses a degree of non-linearity. Therefore, the potential of applying fuzzy logic in this area might be promising. Two fuzzy-logic models were developed and evaluated using accident data and the corresponding traffic data collected from 123 sections of highway in Pennsylvania from 1984 to 1986. The models use skid number, posted speed, average daily traffic, percentage of wet time, and driving difficulty as input variables and the number of wet-pavement accidents as the output variable. The first model is based on Mamdani’s fuzzy-inference method, and the second is a Sugeno-type fuzzy-logic model using the fuzzy-clustering method. The two fuzzy-logic models show superiority over the probabilistic model and the nonlinear regression model. Results indicate that, in addition to predicting the risk of wet-pavement accidents, the fuzzy-logic model can be applied conveniently to determine specific corrective actions that should be undertaken to improve safety.

scholarly journals River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2427
Author(s):  
Fatih Üneş ◽  
Mustafa Demirci ◽  
Martina Zelenakova ◽  
Mustafa Çalışıcı ◽  
Bestami Taşar ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
River Flow ◽  
Fuzzy Inference ◽  
Accurate Determination ◽  
Daily Rainfall ◽  
Fuzzy Rules ◽  
Membership Functions ◽  
Inference System ◽  
Flow Estimation ◽  
Generation Technique

Accurate determination of river flows and variations is used for the efficient use of water resources, the planning of construction of water structures, and preventing flood disasters. However, accurate flow prediction is related to a good understanding of the hydrological and meteorological characteristics of the river basin. In this study, flow in the river was estimated using Multi Linear Regression (MLR), Artificial Neural Network (ANN), M5 Decision Tree (M5T), Adaptive Neuro-Fuzzy Inference System (ANFIS), Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) models. The Stilwater River in the Sterling region of the USA was selected as the study area and the data obtained from this region were used. Daily rainfall, river flow, and water temperature data were used as input data in all models. In the paper, the performance of the methods is evaluated based on the statistical approach. The results obtained from the generated models were compared with the recorded values. The correlation coefficient (R), Mean Square Error (MSE), and Mean Absolute Error (MAE) statistics are computed separately for each model. According to the comparison criteria, as a final result, it is considered that Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model have better performance in river flow estimation than the other models.

A Fuzzy Logic Model to Predict Coral Reef Development under Nutrient and Sediment Stress

1998 ◽  
Vol 12 (5) ◽  
pp. 957-965 ◽  
Author(s):  
Erik H. Meesters ◽  
Rolf P. M. Bak ◽  
Susie Westmacott ◽  
Mark Ridgley ◽  
Steve Dollar
Keyword(s):  
Fuzzy Logic ◽  
Coral Reef ◽  
Logic Model ◽  
Reef Development ◽  
Fuzzy Logic Model

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 Logic Model for Hourly Electrical Power Demand Modeling

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 448
Author(s):  
Marco Antonio Islas ◽  
José de Jesús Rubio ◽  
Samantha Muñiz ◽  
Genaro Ochoa ◽  
Jaime Pacheco ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
New England ◽  
Electrical Power ◽  
Logic Model ◽  
Power Demand ◽  
Demand Modeling ◽  
Mapping Model ◽  
Fuzzy Logic Model ◽  
Radial Basis

In this article, a fuzzy logic model is proposed for more precise hourly electrical power demand modeling in New England. The issue that exists when considering hourly electrical power demand modeling is that these types of plants have a large amount of data. In order to obtain a more precise model of plants with a large amount of data, the main characteristics of the proposed fuzzy logic model are as follows: (1) it is in accordance with the conditions under which a fuzzy logic model and a radial basis mapping model are equivalent to obtain a new scheme, (2) it uses a combination of the descending gradient and the mini-lots approach to avoid applying the descending gradient to all data.

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