scholarly journals State Aviation Risk Assessment Level Determination Using Hierarchical Fuzzy Inference System Based on Cognitive Maps

2021 ◽  
Vol 3 (1) ◽  
pp. 1-15
Author(s):  
Alvimar de Lucena Costa Junior ◽  
◽  
Mischel Carmen Neyra Belderrain ◽  
Moacyr Machado Cardoso Junior ◽  
◽  
...  
Keyword(s):  
Risk Assessment ◽  
Cognitive Mapping ◽  
Fuzzy Inference ◽  
Aviation Safety ◽  
Civil Aviation ◽  
Problem Structuring ◽  
Inference System ◽  
Risk Levels ◽  
Inference Systems ◽  
Aviation Risk

During 2018, ICAO (International Civil Aviation Organization, a specialized UN organization) made available the results of its USOAP (Universal Safety Oversight Audit Program): the ratio of compliance for each ICAO member State to 1047 aviation safety-related protocol questions, divided into eight audit areas. Numbers itself has little meaning, even for aviation personnel. Using Cognitive Mapping (CogMap), a Problem Structuring Method tool, this paper develops a framework to extract and organize information from aviation specialists, allowing define Risk Assessment Level for each State, and for each Aviation Safety Branch defined. Using Fuzzy Inference Systems (FIS), helpful supporting decision making, Big Data available from ICAO is converted to Risk Levels for each State and audit area, what may be used to make informed better Safety decisions on the World Aviation Market. Up to the moment, there’s no evidence on the literature of using CogMap to establish a FIS.

scholarly journals PROPOSING A NEW METHODOLOGY BASED ON FUZZY LOGIC FOR TUNNELLING RISK ASSESSMENT

2014 ◽  
Vol 20 (1) ◽  
pp. 82-94 ◽  
Author(s):  
Abdolreza Yazdani-Chamzini
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference ◽  
Assessment Model ◽  
Tunnel Construction ◽  
Construction Operations ◽  
Protective Measures ◽  
Inference System ◽  
Proposed Model ◽  
Mine Development ◽  
Civil Defence

Tunnels are artificial underground spaces that provide a capacity for particular goals such as storage, under-ground transportation, mine development, power and water treatment plants, civil defence. This shows that the tunnel construction is a key activity in developing infrastructure projects. In many situations, tunnelling projects find themselves involved in the situations where unexpected conditions threaten the continuity of the project. Such situations can arise from the prior knowledge limited by the underground unknown conditions. Therefore, a risk analysis that can take into account the uncertainties associated with the underground projects is needed to assess the existing risks and prioritize them for further protective measures and decisions in order to reduce, mitigate and/or even eliminate the risks involved in the project. For this reason, this paper proposes a risk assessment model based on the concepts of fuzzy set theory to evaluate risk events during the tunnel construction operations. To show the effectiveness of the proposed model, the results of the model are compared with those of the conventional risk assessment. The results demonstrate that the fuzzy inference system has a great potential to accurately model such problems.

scholarly journals Utilization of Current Data for Geospatial Analysis of the Appropriateness of Apple Plantation Land Based on Fuzzy Inference Systems

2019 ◽  
Vol 4 (1) ◽  
pp. 64
Author(s):  
Prayudi Lestantyo
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Geospatial Analysis ◽  
Current Data ◽  
Inference System ◽  
Apple Industry ◽  
Accuracy Test ◽  
Spatial Factor ◽  
Apple Production ◽  
Inference Systems

Apple is a high-value import fruit in Indonesia. One of the Apple production centers in Indonesia is Batu City, but the results tend to be declining in every year. To fulfill the demand of domestic apple industry, it is than a must to open new plantation land by observing the spatial factor. Expert and direct field review are needed to perform the analysis of land suitability, so that it will takes a lot of time and effort. Therefore, a smart system that can conduct geospatial analysis by using fuzzy inference system is developed. The data was obtained by using satellite imagery, data interpolation, and digitized and then analyzed into information. The analysis was performed on each pixel with six variable inputs including altitude, rainfall, humidity, air temperature, soil type and sun shine intensity. Besides that, the five-clustering output makes the results more accurate. From the results of the accuracy test, it is obtained a 92,86% accuracy, by comparing the results of the spatial analysis using fuzzy inference system with direct review on the field.

Fuzzy Logic-Based Inference Systems

2015 ◽  
pp. 209-239
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Mathematical Formulation ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Adaptive Networks ◽  
Inference System ◽  
Inference Systems

This chapter presents the mathematical formulation of the fuzzy logic-based inference systems, used as means to infer about the response of ill-conditioned systems, based on the field knowledge representation in the fuzzy world. Particular approaches are explored, e.g., Fuzzy Inference System (FIS), Adaptive Networks-based FIS (ANFIS), Intuitionistic FIS (IFIS) and Fuzzy Cognitive Map (FCM), surfacing their potentialities in modeling applications, such as those in the field of learning, examined in the chapters of Part III that follow.

Stochastic Methods for Estimation and Problem Solving in Engineering

2018 ◽  
pp. 139-160
Author(s):  
Tetiana Shmelova
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Emergency Situation ◽  
Civil Aviation ◽  
Stochastic Methods ◽  
Navigation Systems ◽  
International Civil Aviation Organization ◽  
Risk And Uncertainty ◽  
Risk Levels ◽  
Professional Activities

In this chapter, the author presents stochastic methods in aviation. The stochastic methods are presented as methods of decision making (DM) of operators of air navigation systems (ANS) in risk and uncertainly. The ANS is presented as a socio-technical system (STS). Analysis influences the factors of professional and non-professional activities on DM of STS's operators. The author made an analysis of the International Civil Aviation Organization (ICAO) documents on risk assessment. To determine the quantitative characteristics of risk levels, models for DM by the operator of the aviation system under risk and uncertainty have been developed. The author demonstrates some interesting advantages offered by the new methodology of DM in STS and forecasting the behavior of the operator in an emergency situation (ES) for using models of DM in risk and uncertainty.

Artificial neural networks and adaptive neuro-fuzzy inference systems for parameter identification of dynamic systems

2020 ◽  
Vol 39 (5) ◽  
pp. 6145-6155
Author(s):  
Ramin Vatankhah ◽  
Mohammad Ghanatian
Keyword(s):  
Differential Equations ◽  
Dynamic Systems ◽  
Fuzzy Inference ◽  
Dynamic Equations ◽  
Control Analysis ◽  
Unknown Parameters ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Inference Systems

There would always be some unknown geometric, inertial or any other kinds of parameters in governing differential equations of dynamic systems. These parameters are needed to be numerically specified in order to make these dynamic equations usable for dynamic and control analysis. In this study, two powerful techniques in the field of Artificial Intelligence (AI), namely Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to explain how unknown parameters in differential equations of dynamic systems can be identified. The data required for training and testing the ANN and the ANFIS are obtained by solving the direct problem i.e. solving the dynamic equations with different known parameters and input stimulations. The governing ordinary differential equations of the system is numerically solved and the output values in different time steps are obtained. The output values of the system and their derivatives, the time and the inputs are given to the ANN and the ANFIS as their inputs and the unknown parameters in the dynamic equations are estimated as the outputs. Finally, the performances of the ANN and the ANFIS for identifying parameters of the system are compared based on the test data Percent Root Mean Square Error (% RMSE) values.

A hybrid risk assessment method for mining sector based on QFD, fuzzy inference system, and AHP

2020 ◽  
Vol 39 (5) ◽  
pp. 6047-6058
Author(s):  
Ulas Cinar ◽  
Selcuk Cebi
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference System ◽  
Quality Function Deployment ◽  
Fuzzy Inference ◽  
Assessment Method ◽  
Working Environment ◽  
Industrial Safety ◽  
Mining Sector ◽  
Inference System ◽  
Conventional Methods

Conventional risk assessment methods are widely used for industrial safety applications. However, there are serious obstacles to their usage as; (i) all of the potential hazards are considered as an independent event, (ii) various risks are identified based on these hazards, (iii) risk magnitudes of these risks are obtained without considering interdependencies among the hazards, and then (iv) the protective measures against the defined risks are taken based on these risk magnitudes. Therefore, conventional methods do not provide any assessment for overall risks in the working environment. Furthermore, although an accident may cause different severity such as loss of working days, loss of limbs, occupational disease, and death, the conventional methods do not consider all potential consequences of any accident, simultaneously. The main objective of this paper is to propose an effective risk assessment approach by using the fuzzy set theory, Analytical Hierarchy Process (AHP), Fuzzy Inference System (FIS), and Quality Function Deployment (QFD) methods to quantify the risk of any hazard considering interdependencies among all potential hazards and consequences in working environment. Within the scope of this research, an application in the mining sector has been presented to illustrate the validation and the effectiveness of the proposed approach**.

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