scholarly journals A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 44 ◽  
Author(s):  
Muhammad Fayaz ◽  
Quoc Bao Pham ◽  
Nguyen Thi Thuy Linh ◽  
Pham Thi Thao Nhi ◽  
Dao Nguyen Khoi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Water Supply ◽  
Manufacturing Industry ◽  
Fuzzy Inference ◽  
Residential Buildings ◽  
Risk Index ◽  
The People ◽  
Fuzzy Logic Method ◽  
Do It Yourself ◽  
Water Supply Pipeline

The standard manufacturing organizations follow certain rules. The highest ubiquitous organizing principles in infrastructure design are modular idea and symmetry, both of which are of the utmost importance. Symmetry is a substantial principle in the manufacturing industry. Symmetrical procedures act as the structural apparatus for manufacturing design. The rapid growth of population needs outstrip infrastructure such as roads, bridges, railway lines, commercial, residential buildings, etc. Numerous underground facilities are also installed to fulfill different requirements of the people. In these facilities one of the most important facility is water supply pipelines. Therefore, it is essential to regularly analyze the water supply pipelines’ risk index in order to escape from economic and human losses. In this paper, we proposed a simplified hierarchical fuzzy logic (SHFL) model to reduce the set of rules. To this end, we have considered four essential factors of water supply pipelines as input to the proposed SHFL model that are: leakage, depth, length and age. Different numbers of membership functions are defined for each factor according to its distribution. The proposed SHFL model takes only 95 rules as compared to the traditional mamdani fuzzy logic method that requires 1225 rules. It is very hard and time consuming for experts to design 1225 rules accurately and precisely. Further, we proposed a Do-it-Yourself (DIY) system for the proposed SHFL method. The purpose of the DIY system is that one can design the FIS model according to his or her need.

scholarly journals Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

Processes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 182 ◽  
Author(s):  
Muhammad Fayaz ◽  
Shabir Ahmad ◽  
Lei Hang ◽  
DoHyeun Kim
Keyword(s):  
Water Supply ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Residential Buildings ◽  
Risk Index ◽  
Inference System ◽  
Full Structure ◽  
Implementation Time ◽  
Novel Method ◽  
Water Supply Pipeline

As populations grow, facilities such as roads, bridges, railways lines, commercial and residential buildings, etc., must be expanded and maintained. There are extensive networks of underground facilities to fulfil the demand, such as water supply pipelines, sewage pipelines, metro structures, etc. Hence, a method to regularly assesses the risk of the underground facility failures is needed to decrease the chance of accidental loss of service or accidents that endanger people and facilities. In the proposed work, a cohesive hierarchical fuzzy inference system (CHFIS) was developed. A novel method is proposed for membership function (MF) determination called the heuristic based membership functions determination (HBMFD) method to determine an appropriate MF set for each fuzzy logic method in CHFIS. The proposed model was developed to decrease the number of rules for the full structure fuzzy inference system with all rule implementation. Four very crucial parameters were considered in the proposed work that are inputs to the proposed CHFIS model in order to calculate the risk of water supply pipelines. In order to fully implement the proposed CHFIS just 85 rules are needed while using the traditional Mamdani fuzzy inference system, 900 rules are required. The novel method greatly reduces implementation time and rule design sets that are extremely time consuming to develop and difficult to maintain.

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 ◽  
pp. 1289-1305
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 ◽  
Fuzzy Logic Method ◽  
The Neural Networks

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.

Neural Network and Fuzzy Logic Diagnostics of 1X Faults in Rotating Machinery

2005 ◽  
Author(s):  
A. El-Shafei ◽  
T. A. F. Hassan ◽  
A. K. Soliman ◽  
Y. Zeyada ◽  
N. Rieger
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Vector Quantization ◽  
Fuzzy Inference ◽  
Rotating Machinery ◽  
Test Rig ◽  
Fuzzy Inference Engine ◽  
In Series ◽  
Fuzzy Logic Method

In this paper, the application of Neural Networks and Fuzzy Logic to the diagnosis of Faults in Rotating Machinery is investigated. The Learning-Vector-Quantization (LVQ) Neural Network is applied in series and in parallel to a Fuzzy inference engine, to diagnose 1x faults. The faults investigated are unbalance, misalignment, and structural looseness. The method is applied to a test rig [1], and the effectiveness of the integrated Neural Network and Fuzzy Logic method is illustrated.

scholarly journals The Obstacles Detector with Tahani Fuzzy Logic as The Tool for Blind People

2018 ◽  
pp. 72
Author(s):  
Abdurrasyid . ◽  
Rakhmat Arianto ◽  
Indrianto Indrianto ◽  
Bramantyo Adi Nugroho
Keyword(s):  
Fuzzy Logic ◽  
Scientific Method ◽  
School Age Children ◽  
Raspberry Pi ◽  
Blind People ◽  
Flat Surfaces ◽  
The People ◽  
Fuzzy Logic Method ◽  
Audio Output ◽  
Audio Information

Indonesian Blind Union said that the number of blind people in Indonesia reached 3.75 million and 40% in school-age children, and this number will continue to increase each year. Blind people will need the tool to help their day to day activities. The research that has been developed still have flaws, whereas they do not provide the sound of information to the people with visual impairment about the obstacle, included no scientific method used in the research, especially about how the appliance works. This research does not only provide ‘beep’ sound when obstacles are detected, but also provides audio information through a headset to the blind people. There are three obstacles detected, they are holes, bumps, and walls, and it can help the blind people to decide whether to dodge or to step high. To support the audio output and the design processing speed of the appliance, this research uses Raspberry Pi 3 mini PC, three ultrasonic sensors that are used to detect obstruction objects upright, hole and bump, and to initialize the initial values before it detects the obstruction. Tahani fuzzy logic method used to different obstacles such as the bumps, flat surfaces, or holes so the blind people feel much safer while walking.  

scholarly journals A Blended Risk Index Modeling and Visualization Based on Hierarchical Fuzzy Logic for Water Supply Pipelines Assessment and Management

Processes ◽  
2018 ◽  
Vol 6 (5) ◽  
pp. 61 ◽  
Author(s):  
Muhammad Fayaz ◽  
Shabir Ahmad ◽  
Israr Ullah ◽  
DoHyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Water Supply ◽  
Risk Index

scholarly journals Penerapan Sistem Fuzzy Tsukamoto Dalam Memperkirakan Hasil Produksi Padi

2020 ◽  
Vol 1 (2) ◽  
pp. 126-130
Author(s):  
Lena Sapura ◽  
Agiffuddinsyah Sinaga ◽  
Firdaus Siahaan
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Rice Production ◽  
Rice Fields ◽  
Rice Seed ◽  
Inference System ◽  
Goods And Services ◽  
Short Period ◽  
Fuzzy Logic Method

Plantations are all activities that commercialize certain plants on the soil and / or other growing media in suitable ecosystems; process and market the goods and services produced by these plants, with the help of science and technology, capital and management to create prosperity for plantation businesses and the community. In ordinary or more dominant plantations, rice fields with sufficient yields and even processing with a fairly short period of time, especially in rice. Rice is the main or basic need and source of calories for humans. Factors that affect rice production include land, seeds, weather, and fertilizer, these factors clearly affect the quality and amount of production produced by farmers. The fuzzy logic method applies Tsukamto's fuzzy inference system in estimating rice production with the variables that influence it. The research objective is to estimate how much rice production with the Tsukamoto method of fuzzy inference using AND operations based on land variables, rice seed material, fertilizer, and the amount of production.

scholarly journals An Efficient Methodology for Water Supply Pipeline Risk Index Prediction for Avoiding Accidental Losses

2020 ◽  
Vol 11 (5) ◽  
Author(s):  
Muhammad Shuaib Qureshi ◽  
Ayman Aljarbouh ◽  
Muhammad Fayaz ◽  
Muhammad Bilal ◽  
Wali Khan ◽  
...  
Keyword(s):  
Water Supply ◽  
Risk Index ◽  
Water Supply Pipeline

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

Ensuring of irrigation machines application depth in uniformity’s distribution

2020 ◽  
pp. 25-30
Author(s):  
Gennadiy Ol'garenko ◽  
Boris Gordon
Keyword(s):  
Water Supply ◽  
Irrigation Depth ◽  
Application Depth ◽  
Water Supply Pipeline

A method of rain uniformity’s distribution was presented for different spraying devices, which were set on irrigation machines working in different mode of moving. A method for effective irrigation radius and width calculation was justified by using irrigation depth uniformity values from the area under water supply pipeline of the irrigation machine.

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