Managing the process of passenger security control at an airport using the fuzzy inference system

Skorupski, Jacek; Uchroński, Piotr

doi:10.1016/j.eswa.2015.11.014

Cited by 32 publications

(10 citation statements)

References 36 publications

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“…The September 11 events led to many fundamental operational and aviation security policies at all commercial airports [10]. Skorupski ve Uchronski (2016) stated that the transportation systems, which are considered critical infrastructure parts, are constantly at risk of terrorist attacks and that although the airport terminals are well protected, they are a frequent target for these attacks [11]. The development of the civil aviation network has made air transport the primary target for terrorist activities [12].…”

Section: Security Checkmentioning

confidence: 99%

Departure Passenger Flow Simulation: Case Study on Izmir Adnan Menderes Airport Pre and Post COVID-19

Ateş

Koç²,

Koç³

et al. 2021

International Journal of Applied Mathematics Electronics and Computers

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The flow of departing passengers from the airport inside the terminal is affected by strategic and tactical decisions of airport operation teams. The decisions made by the managers regarding opening and closing times of check-in, boarding, and gates are critical to optimize the performance of the terminal operation. In this research, Izmir Adnan Menderes Airport was taken as a use case. Departure terminal building of check-in, security control, and boarding gates was taken as the focus entities. The simulation passenger input data were produced for check-in, security, and boarding gates using different distribution probability methodologies to match the actual data. Three days of data (300 flights, 3 thousand passengers) has been derived with the help of a custom generator. A unique simulation tool called Departure Passenger Flow Simulator (DPFs) has been developed for research. Due to manage passenger flow pre and post COVID-19, the opening and closing times of assets (check-in and gates) were simulated in 6 iterations in DPFs in 3 different scenarios. As a result of the research, it was understood that the resources for Izmir Adnan Menderes Airport were sufficient, considering the COVID-19 measures published by the European Aviation Safety Agency (EASA) and the General Directorate of Civil Aviation.

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Section: Security Checkmentioning

confidence: 99%

Departure Passenger Flow Simulation: Case Study on Izmir Adnan Menderes Airport Pre and Post COVID-19

Ateş

Koç²,

Koç³

et al. 2021

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

show abstract

“…For example, consider the decimal number 502 10 Azim, Jaffar, & Mirza, 2014;Danisman, Bilasco, & Martinet, 2015;Jagadeesh, Kumar, & Reddy, 2015;Ghanei & Faez, 2016. Risk assessment and safety Camastra et al, 2015;Rudnik & Deptula, 2015;Skorupski & Uchronski, 2015;Hettiarachchi, Do, & Choi, 2016;Nadimi, Behbahani, & Shahbazi, 2016;Skorupski & Uchronski, 2016;Zhang, Yan, Zhang, Yang, & Wang, 2016. Location selection Budak & Ustundag, 2015;Oussalah, Alakhras, & Hussein, 2015. again.…”

Section: Number Systemsmentioning

confidence: 99%

A simple and effective remedial learning system with a fuzzy expert system

Lin

Guo

Lin

2016

Computer Assisted Learning

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This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate the success of the proposed system where 88 students who studied a computer-related program at a vocational high school in Taiwan participated in the experiment. The participating students came from three different types of classes. The fuzzy expert system determined the numbers of the remedial material units according to the scores of the pre-test. Based on the results of the fuzzy expert system, each student then received personalized remedial learning materials by randomly selecting problem-based learning units from a learning material repository. After reading the remedial learning materials, the students took the post-test. The experimental results reveal that the students made significant progresses after studying the remedial learning materials. Both of high-achieving students and low-achieving students made significant progresses. Moreover, all of the three types of students made significant progresses.

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“…Therefore, a fuzzy control system is proposed to determinate when a bathroom needs cleaning. Fuzzy control systems have been applied in the field of airports [42][43][44]. However, none of them are applied to the task of bathroom cleaning.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Approach Using LoRaWAN Devices: An Airport Case Study

et al. 2020

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The current situation with COVID-19 is changing our courses of action toward ensuring health security. This is particularly crucial in airports, which usually receive more than 300,000 travellers in one single day. In this work, we present an Internet of Things (IoT) network to monitor the status of toilets and improve their maintenance. The system is based on IoT networks with different sensors to control soap levels, room capacity, distances, temperature, and humidity. This information is processed by a multi-agent system that detects possible anomalies and makes decisions accordingly. A case study in a real environment is developed in order to demonstrate the usefulness of the system. The results show that the proposed method can be used to successfully manage and control airport toilets.

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Managing the process of passenger security control at an airport using the fuzzy inference system

Cited by 32 publications

References 36 publications

Departure Passenger Flow Simulation: Case Study on Izmir Adnan Menderes Airport Pre and Post COVID-19

Departure Passenger Flow Simulation: Case Study on Izmir Adnan Menderes Airport Pre and Post COVID-19

A simple and effective remedial learning system with a fuzzy expert system

Multi-Agent Approach Using LoRaWAN Devices: An Airport Case Study

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