scholarly journals Measuring flood resilience: a fuzzy logic approach

2017 ◽  
Vol 35 (5) ◽  
pp. 470-487 ◽  
Author(s):  
Victor Oluwasina Oladokun ◽  
David G. Proverbs ◽  
Jessica Lamond
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Business Case ◽  
Individual Property ◽  
Inference System ◽  
Content Type ◽  
Wide Range ◽  
Resilience Index ◽  
Fuzzy Logic Approach ◽  
The Individual

Purpose Flood resilience is emerging as a major component of an integrated strategic approach to flood risk management. This approach recognizes that some flooding is inevitable and aligns with the concept of “living with water.” Resilience measurement is a key in making business case for investments in resilient retrofits/adaptations, and could potentially be used to inform the design of new developments in flood prone areas. The literature is, however, sparse on frameworks for measuring flood resilience. The purpose of this paper is to describe the development of a fuzzy logic (FL)-based resilience measuring model, drawing on a synthesis of extant flood resilience and FL literature. Design/methodology/approach An abstraction of the flood resilience system followed by identification and characterization of systems’ variables and parameters were carried out. The resulting model was transformed into a fuzzy inference system (FIS) using three input factors: inherent resilience, supportive facilities (SF) and resident capacity. Findings The resulting FIS generates resilience index for households with a wide range of techno-economic and socio-environmental features. Originality/value It is concluded that the FL-based model provides a veritable tool for the measurement of flood resilience at the level of the individual property, and with the potential to be further developed for larger scale applications, i.e. at the community or regional levels.

Benchmarking fuzzy logic and ANFIS approaches for leanness evaluation in an Indian SME

2017 ◽  
Vol 24 (4) ◽  
pp. 973-993 ◽  
Author(s):  
Rohit Agrawal ◽  
P. Asokan ◽  
S. Vinodh
Keyword(s):  
Fuzzy Logic ◽  
Lean Manufacturing ◽  
Fuzzy Inference ◽  
Evaluation Model ◽  
Medium Size ◽  
Inference System ◽  
Content Type ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Leanness Evaluation

Purpose The purpose of this paper is to present a study that is focused on application of fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) approaches for leanness evaluation in an Indian small- and medium-size enterprise (SME). Design/methodology/approach Lean manufacturing concepts are being adopted by SMEs to sustain in the competitive manufacturing landscape. Performance of lean system needs to be assessed using appropriate methods. A model for measuring lean performance is proposed with five enablers, 30 criteria and 90 attributes. Leanness index is computed using fuzzy logic approach and benchmarked with ANFIS approach. Findings Leanness index computed using fuzzy logic approach is found to be (4.47, 5.97, 7.55) and that of ANFIS approach is found to be 5.84 to facilitate benchmarking of leanness evaluation. After finding weaker areas, certain improvement initiatives are being deployed. Research limitations/implications The developed model for leanness evaluation has been test implemented in an SME. In future, the model could be test implemented in several SMEs. Practical implications A case study conducted in an SME involved in heavy engineering fabrication is presented. Therefore, the inferences derived from the study has practical propensity. Originality/value The development of leanness evaluation model for SMEs and deployment in an industrial scenario are the original contributions of the authors.

Fuzzy logic approach for investigation of microstructure and mechanical properties of Sn96.5-Ag3.0-Cu0.5 lead free solder alloy

2017 ◽  
Vol 29 (4) ◽  
pp. 191-198 ◽  
Author(s):  
Muhammad Aamir ◽  
Izhar Izhar ◽  
Muhammad Waqas ◽  
Muhammad Iqbal ◽  
Muhammad Imran Hanif ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Fuzzy Logic ◽  
Solder Alloy ◽  
Thermal Aging ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Content Type ◽  
Wide Range ◽  
Fuzzy Logic Approach ◽  
Free Solder

Purpose This paper aims to develop a fuzzy logic-based algorithm to predict the intermetallic compound (IMC) size and mechanical properties of soldering material, Sn96.5-Ag3.0-Cu0.5 (SAC305) alloy, at different levels of temperature. The reliability of solder joint in materials selection is critical in terms of temperature, mechanical properties and environmental aspects. Owing to a wide range of soldering materials available, the selection space finds a fuzzy characteristic. Design/methodology/approach The developed algorithm takes thermal aging temperature for SAC305 alloy as input and converts it into fuzzy domain. These fuzzified values are then subjected to a fuzzy rule base, where a set of rules determines the IMC size and mechanical properties, such as yield strength (YS) and ultimate tensile strength (UTS) of SAC305 alloy. The algorithm is successfully simulated for various input thermal aging temperatures. To analyze and validate the developed algorithm, an SAC305 lead (Pb)-free solder alloy is developed and thermally aged at 40, 60 and 100°C temperature. Findings The experimental results indicate an average IMCs size of 5.967 (in Pixels), 19.850 N/mm2 YS and 22.740 N/mm2 UTS for SAC305 alloy when thermally aged at an elevated temperature of 140°C. In comparison, the simulation results predicted 5.895 (in Pixels) average IMCs size, 19.875 N/mm2 YS and 22.480 N/mm2 UTS for SAC305 alloy at 140°C thermally aged temperature. Originality/value From the experimental and simulated results, it is evident that the fuzzy-based developed algorithm can be used effectively to predict the IMCs size and mechanical properties of SAC305 at various aging temperatures, for the first time.

scholarly journals A Mamdani Type Fuzzy Inference System to Calculate Employee Susceptibility to Phishing Attacks

2021 ◽  
Vol 11 (19) ◽  
pp. 9083
Author(s):  
Yahya Lambat ◽  
Nick Ayres ◽  
Leandros Maglaras ◽  
Mohamed Amine Ferrag
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Human Error ◽  
Fuzzy Inference ◽  
Social Engineering ◽  
Rule Base ◽  
Inference System ◽  
Phishing Attacks ◽  
Future System ◽  
The Individual

It is a well known fact that the weakest link in a cyber secure system is the people who configure, manage or use it. Security breaches are persistently being attributed to human error. Social engineered based attacks are becoming more sophisticated to such an extent where they are becoming increasingly more difficult to detect. Companies implement strong security policies as well as provide specific training for employees to minimise phishing attacks, however these practices rely on the individual adhering to them. This paper explores fuzzy logic and in particular a Mamdani type fuzzy inference system to determine an employees susceptibility to phishing attacks. To negate and identify the susceptibility levels of employees to social engineering attacks a Fuzzy Inference System FIS was created through the use of fuzzy logic. The utilisation of fuzzy logic is a novel way in determining susceptibility due to its ability to resemble human reasoning in order to solve complex inputs, or its Interpretability and simplicity to be able to compute with words. This proposed fuzzy inference system is based on a number of criteria which focuses on attributes relating to the individual employee as well as a companies practices and procedures and through this an extensive rule base was designed. The proposed scoring mechanism is a first attempt towards a holistic solution. To accurately predict an employees susceptibility to phishing attacks will in any future system require a more robust and relatable set of human characteristics in relation to the employee and the employer.

An integrated Delphi – fuzzy logic approach for measuring supply chain resilience: an illustrative case from manufacturing industry

2019 ◽  
Vol 23 (3) ◽  
pp. 350-375 ◽  
Author(s):  
Siva Kumar ◽  
Ramesh Anbanandam
Keyword(s):  
Fuzzy Logic ◽  
Supply Chain ◽  
Manufacturing Industry ◽  
Illustrative Case ◽  
Organizational Relationships ◽  
Content Type ◽  
Management Culture ◽  
Resilience Index ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Purpose Growth in a number of the supply chain (SC) disruptions threatens the enterprises globally. Earlier studies and reports say that many organizations go out of businesses within two or three years after they experience a major disruption. Therefore, companies in today’s volatile business arena need to possess the necessary resilience level to combat supply china disruptions. This is even more important for organizations of developing nations, which are constantly struggling to gain the advantages of globalization and to grab the new opportunities. Thus, this paper aims to help organizations understand their SC resilience level through a framework. Design/methodology/approach The methodology comprises integrated Delphi – fuzzy logic approach in identifying formative elements of SC resilience from a diverse resilience related body of knowledge and distinguish key obstacles of SC resilience based on their performance level. Findings Findings reveal that SC flexibility components such as sourcing, manufacturing and logistic flexibility are the major contributors of SC resilience index of case organization. Similarly, lack of risk management culture, inter-organizational relationships, information sharing and integration of SC stakeholders are the major inhibitors of resilience. Thus, the organization needs to overcome these identified obstacles to enhance their SC resilience level. Practical implications Present study offers a novel focus of research on SC resilience measurement that is significant for understanding the level of immunity enterprises possess to unanticipated SC interruptions, and the ability to bounce back after an unforeseen event. Originality/value This paper proposes an integrated Delphi – fuzzy logic framework for measuring SC resilience. In doing so, the study identifies key potential inhibitors of SC resilience of the case company under study.

scholarly journals Aplikasi Fuzzy Logic dalam Diagnosa Penyakit Diabetes Mellitus pada PUSKESMAS di Jakarta Timur

2016 ◽  
Vol 2 (3) ◽  
pp. 21-30 ◽  
Author(s):  
Zaimatun Niswati ◽  
Aulia Paramita ◽  
Fanisya Alva Mustika
Keyword(s):  
Diabetes Mellitus ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fuzzy Inference ◽  
Treatment Decision ◽  
Inference System ◽  
Family History Of Diabetes ◽  
Patients With Diabetes ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Abstract— Patients with diabetes mellitus increased from year to year. This is due to delays in diagnosis of the disease and also because of unhealthy lifestyles. This study aims to create an application of decision support systems in the field of health, namely the diagnosis of the disease Diabetes Mellitus with Fuzzy Inference System (FIS) Mamdani, so that a layman can perform early diagnosis and immediate treatment. Decision Support System Techniques developed to improve the effectiveness of decision-makers. Samples are six puskesmas in East Jakarta. This application uses five variables as inputs consisting of glucose 2 hours after a meal, Diastolic blood pressure, body mass index, family history of diabetes, total pregnancies and one variable as output. The data obtained be processed using fuzzy logic approach to programming Matlab and made Graphical User Interface (GUI). Result is an expert system for diagnosis of Diabetes Mellitus .The trial results by midwives and nurses puskesmas is 100% of these applications in accordance with the doctor”s diagnosis. It is to help improve the quality of service in the Puskesmas in East Jakarta, thus satisfying the users and puskesmas be able to compete both nationally and internationally. 

Analyzing of micro-electro-mechanical systems (MEMS) sensor for pumping aggregates

Sensor Review ◽  
2018 ◽  
Vol 38 (2) ◽  
pp. 194-198
Author(s):  
Milos Milovancevic ◽  
Edvard Tijan
Keyword(s):  
Fuzzy Inference ◽  
Mechanical Systems ◽  
Vibration Monitoring ◽  
Micro Electro Mechanical Systems ◽  
Inference System ◽  
Content Type ◽  
Mems Sensor ◽  
Wide Range ◽  
Smart Mobile Phone ◽  
Smart Mobile

Purpose The purpose of this research paper is to develop and analyze micro-electro-mechanical systems sensor for vibration monitoring of pumping aggregate. Design/methodology/approach The system is based on smart sensor and smart mobile phone. Findings The numerous measurements on a wide range of turbo aggregates were performed to establish the operating condition of pumping aggregates. Originality/value Afterwards, the influence of vibration at different positions on the output vibration of the pumping aggregate was analyzed by adaptive neuro fuzzy inference system method.

scholarly journals Course recommendation system using fuzzy logic approach

2020 ◽  
Vol 17 (1) ◽  
pp. 365
Author(s):  
Mohd Suffian Sulaiman ◽  
Amylia Ahamad Tamizi ◽  
Mohd Razif Shamsudin ◽  
Azri Azmi
Keyword(s):  
Fuzzy Logic ◽  
Recommendation System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Education Environment ◽  
User Design ◽  
Interest Level ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
The Right

Course selection is a key for success in student’s academic path. In today’s education environment, various courses offered by different academic institutions required the students to explore the course outline manually. Most of them are lacking in knowledge, having dilemma and making blind selections to choose the right course. Therefore, it is essential to have a course recommendation to provide guidance to a student to choose the course related with their interest and skill. This paper proposed to develop a course recommendation system using fuzzy logic approach. The development methodology of this system involves several phases include requirements planning, user design and construction for prototyping, testing and cutover. This study used the fuzzy rules technique in order to calculate each associated student’s skill and interest level based on Mamdani fuzzy inference system method. Then, the rules will generate final outcome which recommend the suitable course path and provide the details to a user based on their course test. The result shows the functionality of this system has been achieved and works well. This study is significantly helping the students to choose their course based on the interest and skill.

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 Fuzzy Logic Approach for Routing in Internet of Things Network

2021 ◽  
Vol 15 (1) ◽  
pp. 18-24
Author(s):  
Reena P. Pingale ◽  
S. N. Shinde
Keyword(s):  
Fuzzy Logic ◽  
Network Lifetime ◽  
Network Performance ◽  
Fuzzy Inference ◽  
Residual Energy ◽  
Link Quality ◽  
Inference System ◽  
Input And Output ◽  
Fuzzy Logic Approach ◽  
Logic Approach

A performance of network is evaluated by considering different parameters. The network lifetime depends on many factors Residual energy, Link lifetime and Delay. The Major Challenge in IoT is to the increased lifetime of low power and lossy network (RPL).The process considering input and output to evaluate Network performance by considering the above factors. The proposed system makes use of FIS (Fuzzy Inference System) for selecting the best path to maximize network lifetime. The outcome obtained by using MATLAB and Network performance is increased. The excellent route is selected if Residual Energy is 194, Link quality is 51.2 and Delay is 1.05 then excellent route quality is 73.4%.

Fuzzy Logic Approach to Wireless Diagnostic Alarms in Anesthesia

2008 ◽  
Author(s):  
D. Benjamin ◽  
H. Gholam Hosseini ◽  
A. Al-Jumaily ◽  
M. J. Harrison
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Physiological State ◽  
Systolic Pressure ◽  
Vital Signs ◽  
Principal Component ◽  
Pressure Variation ◽  
Inference System ◽  
Patient Status ◽  
Fuzzy Logic Approach

This paper explores the use of a fuzzy logic system to continuously monitor a patient’s vital signs during an operation under anesthesia and evaluate the patient’s physiological state. The proposed systems aims to employ statistical-based alarms, principal component analysis (PCA) together with respiratory associated arterial pressure variation (Pulse Pressure Variation-PPV and Systolic Pressure Variation-SPV) and a new fuzzy logic model to estimate the physiological state of the patient under anesthesia. The fuzzy approach will include the recursive fuzzy inference system (RFIS) which combines the current patient status data with the previous output of the inference system thereby reinforcing the current finding based on previous sequential outputs. In addition to this, work is also aimed at the creation of a wireless module that will provide patient information directly to the system.

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