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 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 Regional Collision Risk Prediction System at a Collision Area Considering Spatial Pattern

2021 ◽  
Vol 9 (12) ◽  
pp. 1365
Author(s):  
Ho Namgung ◽  
Joo-Sung Kim
Keyword(s):  
Spatial Pattern ◽  
Risk Prediction ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Prediction System ◽  
Collision Risk ◽  
Inference System ◽  
Optimal Decisions

To reduce the risk of collision in territorial sea areas, including trade ports and entry waterways, and to enhance the safety and efficiency of ship passage, the International Maritime Organization requires the governing body of every country to establish and operate a vessel traffic service (VTS). However, previous studies on risk prediction models did not consider the locations of near collisions and actual collisions and only employed a combined collision risk index in surveillance sea areas. In this study, we propose a regional collision risk prediction system for a collision area considering spatial patterns using a density-based spatial clustering of applications with noise (DBSCAN). Furthermore, a fuzzy inference system based on a near collision (FIS-NC) and long short-term memory (LSTM) is adopted to help a vessel traffic service operator (VTSO) make timely optimal decisions. In the local spatial pattern stage, the ship trajectory was determined by identifying the actual-collision and near-collision locations simultaneously. Finally, the system was developed by learning a sequence dataset from the extracted trajectory of the ship when a collision occurred. The proposed system can recommend an action faster than the fuzzy inference system based on the near-collision location. Therefore, using the developed system, a VTSO can quickly predict ship collision risk situations and make timely optimal decisions at dangerous surveillance sea areas.

scholarly journals IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2802 ◽  
Author(s):  
Qurat-ul Ain ◽  
Sohail Iqbal ◽  
Safdar Khan ◽  
Asad Malik ◽  
Iftikhar Ahmad ◽  
...  
Keyword(s):  
Operating System ◽  
Energy Consumption ◽  
Energy Management ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Residential Buildings ◽  
Inference System ◽  
Intelligent Sensors ◽  
Home Energy Management ◽  
User Comfort

Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.

scholarly journals A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration

2018 ◽  
Author(s):  
Salaheddin Hosseinzadeh
Keyword(s):  
Electron Beam ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Ground Truth ◽  
Motion Blur ◽  
Inverse Filter ◽  
Inference System ◽  
Probe Size ◽  
Novel Method ◽  
The One

This paper presents a novel method of restoring the electron beam (EB) measurements that are degraded by linear motion blur. This is based on a Fuzzy Inference System (FIS) and Wiener inverse filter, together providing autonomy, reliability, flexibility and real-time execution. This system is capable of restoring highly degraded signals without requiring the exact knowledge of EB probe size. The FIS is formed of three inputs, eight fuzzy rules and one output. The FIS is responsible to monitor the restoration results, grade their validity and choose the one that yields to a better grade. These grades are produced autonomously by analyzing results of Wiener inverse filter. To benchmark the performance of the system, ground truth signals obtained using an 18 um wire probe are compared with the restorations. Main aims are therefore a) Provide unsupervised deblurring for device independent EB measurement; b) Improve the reliability of the process; c) Apply deblurring without knowing the probe size. These, further facilitates the deployment and manufacturing of EB probes and probe independent and accurate EB characterization. It also makes restoration of previously collected EB measurements easier, where the probe sizes are not known or recorded.

scholarly journals A Fuzzy Logic-Based Automation toward Intelligent Air Conditioning Systems

2020 ◽  
pp. 335-344
Author(s):  
Gunawan Dewantoro ◽  
Benedictus Edwin Nugraha ◽  
Fransiscus Dalu Setiaji
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Residential Buildings ◽  
Frequency Control ◽  
Air Conditioner ◽  
Fixed Frequency ◽  
Inference System ◽  
Air Conditioners ◽  
Decision Making System ◽  
Air Conditioning Systems

Most of the energy used in residential buildings originates from air conditioners. Meanwhile, air conditioner manufacturers are addressing this issue by the production of efficient air conditioners. However, the convertible frequency air conditioners are expensive, up to 60% higher than the fixed frequency control air conditioners. Besides the human behavior in determining the temperature, setpoint plays an important role regardless of the air conditioners technology used. This study incorporated intelligence in setting up the temperature by means of specially designed remote control. The Tsukamoto fuzzy reasoning was utilized as a decision making system with two inputs, namely the outdoor temperature and the number of occupants. The device used DHT22 as the temperature sensor and HC-SR04 to detect incoming and outgoing occupants. Furthermore, the fuzzy inference system generated infrared signal associated with the temperature setpoint. This signal was received by the air conditioner receiver to adjust the temperature setpoint accordingly. The result of this study showed that the fuzzy inference system determines the temperature setpoint appropriately under variations of surrounding temperature and the number of occupants. The proposed approach yielded a satisfactory perception of thermal comfort and also a promising approach to energy conservation.

scholarly journals A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration

2018 ◽  
Vol 1 (4) ◽  
pp. 48 ◽  
Author(s):  
Salaheddin Hosseinzadeh
Keyword(s):  
Electron Beam ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Ground Truth ◽  
Motion Blur ◽  
Inverse Filter ◽  
Inference System ◽  
Probe Size ◽  
Novel Method ◽  
The One

This paper presents a novel method of restoring the electron beam (EB) measurements that are degraded by linear motion blur. This is based on a fuzzy inference system (FIS) and Wiener inverse filter, together providing autonomy, reliability, flexibility, and real-time execution. This system is capable of restoring highly degraded signals without requiring the exact knowledge of EB probe size. The FIS is formed of three inputs, eight fuzzy rules, and one output. The FIS is responsible for monitoring the restoration results, grading their validity, and choosing the one that yields to a better grade. These grades are produced autonomously by analyzing results of a Wiener inverse filter. To benchmark the performance of the system, ground truth signals obtained using an 18 m wire probe were compared with the restorations. Main aims are therefore: (a) Provide unsupervised deblurring for device independent EB measurement; (b) improve the reliability of the process; and (c) apply deblurring without knowing the probe size. These further facilitate the deployment and manufacturing of EB probes as well as facilitate accurate and probe-independent EB characterization. This paper’s findings also makes restoration of previously collected EB measurements easier where the probe sizes are not known nor recorded.

Sign in / Sign up

Export Citation Format

Share Document