A fuzzy modeling approach to optimize control and decision making in conflict management in air traffic control

Lovato, Agnaldo Volpe; Fontes, Cristiano Hora; Embiruçu, Marcelo; Kalid, Ricardo de Araújo

doi:10.1016/j.cie.2017.11.008

Cited by 16 publications

(7 citation statements)

References 40 publications

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“… Volpe Lovato et al (2018) presented a hybrid model for detecting and resolving conflicts on air traffic routes that used a genetic algorithm and fuzzy logic. The objective was to calculate the optimal actions in terms of changes in flight levels between the aircraft through a global and dynamic analysis.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 spread algorithm in the international airport network-DetArpds

Guevara

Coronel

Maldonado

et al. 2023

PeerJ Computer Science

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Due to COVID-19, the spread of diseases through air transport has become an important issue for public health in countries globally. Moreover, mass transportation (such as air travel) was a fundamental reason why infections spread to all countries within weeks. In the last 2 years in this research area, many studies have applied machine learning methods to predict the spread of COVID-19 in different environments with optimal results. These studies have implemented algorithms, methods, techniques, and other statistical models to analyze the information in accuracy form. Accordingly, this study focuses on analyzing the spread of COVID-19 in the international airport network. Initially, we conducted a review of the technical literature on algorithms, techniques, and theorems for generating routes between two points, comprising an analysis of 80 scientific papers that were published in indexed journals between 2017 and 2021. Subsequently, we analyzed the international airport database and information on the spread of COVID-19 from 2020 to 2022 to develop an algorithm for determining airport routes and the prevention of disease spread (DetARPDS). The main objective of this computational algorithm is to generate the routes taken by people infected with COVID-19 who transited the international airport network. The DetARPDS algorithm uses graph theory to map the international airport network using geographic allocations to position each terminal (vertex), while the distance between terminals was calculated with the Euclidian distance. Additionally, the proposed algorithm employs the Dijkstra algorithm to generate route simulations from a starting point to a destination air terminal. The generated routes are then compared with chronological contagion information to determine whether they meet the temporality in the spread of the virus. Finally, the obtained results are presented achieving a high probability of 93.46% accuracy for determining the entire route of how the disease spreads. Above all, the results of the algorithm proposed improved different computational aspects, such as time processing and detection of airports with a high rate of infection concentration, in comparison with other similar studies shown in the literature review.

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Section: Related Workmentioning

confidence: 99%

COVID-19 spread algorithm in the international airport network-DetArpds

Guevara

Coronel

Maldonado

et al. 2023

PeerJ Computer Science

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“…That is, it supports machinists in doing-right-first-time, and avoiding all the aforementioned drawbacks of poor surface quality during execution. The implementation of fuzzy logic models for predicting and controlling surface quality has raised recently, as this technique gains popularity from its abilities to model uncertainties (Kabini, 2011;Kirby & Chen, 2007;Moreira, et al, 2017;Lovato, et al, 2018). In addition, combined neural networks and fuzzy logic approaches, called neuro-fuzzy approaches, have been developed by (Abburi & Dixit, 2006;Jiao, et al, 2004).…”

Section: Research Idea and Backgroundmentioning

confidence: 99%

“…Furthermore, this work highlights that Adaptive Control (AC) has been introduced as an effective method of optimizing machining parameters online. In recent years, the implementation of fuzzy logic models for predicting and controlling surface roughness has raised as this technique gains popularity from its abilities to model process uncertainties (Kabini, 2011;Kirby & Chen, 2007;Lovato et al, 2018;Moreira et al, 2017). Moreover, Fuzzy Logic Controllers (FLC) have been increasingly applied owing to its successful capabilities in processing linear and highly non-linear systems (Mudi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Supervision controller for real-time surface quality assurance in CNC machining using artificial intelligence

Moreira

et al. 2019

Computers & Industrial Engineering

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“…Continued productivity of CNC machines can be monitored through the ability to model process down time risk. The incorporation of fuzzy logic controllers to assist in controlling and predicting part quality has increased in popularity [24].…”

Section: Introductionmentioning

confidence: 99%

Life Cycle Analysis of Computer Numerical Control (CNC) Machine Components

Kevin¹

2021

AJCST

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The nucleus of this concept and system is directly focused on a 'computer numerical control' (CNC) turret lathe and milling machine tool systems. These concepts focus specifically to this category of engineered systems. Quality design review for quality service systems is a unique concept. Standard product service systems are qualitative and subjective in nature. A quantitative system identifies Key Predictive Attributes (KPA's), which identifies a new concept application technique and applies quantitative methods to these attributes to develop a systemic process of analyzing and monitoring the system. This research is reviewing the specific projection of service outcomes for Machine tool CNC machining centers (Lathes and Milling Machines). The specific key predictive attributes are the elements being utilized in the newly created modular function in this research, to assess the potential impact of discrete elements of these attributes as it affects the occurrence of equipment down time for a system which will work to quantify the service quality of the maintenance process. This project is unique in that currently there is no system which utilizes methods or tools, that proactively gather, analyze, assess, and project outcomes of equipment "Down Time" of the Service Quality process. The innovative position of this analysis is one of actual variable tolerances, versus a more traditional nominal referenced variable reference. What makes this research unique additionally is the system is pre-service and not post service reporting of actual down time of the equipment. This research is much more than pro-forma estimate of service outcomes. Another unique aspect of this method is that it will establish tangible tolerances to assess the performance of the Design Review and Service Quality process and not just rely on subjective nominal values. Mathematical Upper Control Limits (UCL) and Lower Control Limits (LCL) will be programmatically developed based upon the system data. This system tool will develop programming algorithms which will propel this current process from a subjective qualitative process to become a robust quantitative projection tool. The novelty in this research is the development of a quality index through the creation of the new Moriarty/Ranky Transform approach.

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A fuzzy modeling approach to optimize control and decision making in conflict management in air traffic control

Cited by 16 publications

References 40 publications

COVID-19 spread algorithm in the international airport network-DetArpds

COVID-19 spread algorithm in the international airport network-DetArpds

Supervision controller for real-time surface quality assurance in CNC machining using artificial intelligence

Life Cycle Analysis of Computer Numerical Control (CNC) Machine Components

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