Computational Intelligence Methods for Optimising Airport Security Process

University of Technology Sydney. Faculty of Engineering and Information Technology.Airport security screening processes are essential to ensure the safety of both passengers and the aviation industry. Security at airports has improved noticeably in recent years through the utilisation of state-of-the-art technologies and highly trained security officers. However, maintaining a high level of security can be costly to operate and implement. It also lead to delays for passengers and airlines. Nowadays, research is focused to build efficient and effective systems to reduce the congestion caused by the security screening process while maintaining a high level of safety for passengers and the aviation industry. Two open security challenges motivates this thesis: optimize and design the security process at airport, and build an effective intelligent system to detect anomalies in X-ray images. This thesis proposes a series of novel using queuing theory and machine learning models to handle the aforementioned challenges. Particularly, this thesis addresses the issues related to passengers’ congestion at the waiting area and improve the performance of the security detection system to ensure the safety of both passengers and the aviation industry. There are four contributions in this thesis. Contribution 1 proposes queueing theory method to optimise the security screening process with multi-servers operating in parallel to serve a different number of passengers during different seasons, such as Christmas, Easter and school holidays, and time of the day, as this strongly influences the number of passengers. Contribution 2 proposes a novel method based on queueing theory augmented with particle swarm optimisation (QT-PSO) to predict passenger waiting time in a security screening context and to determine the required number of servers and security officers. Contribution 3 propose a tensor-based learning approach to extract the informative latent features that will be used as an input to build a one-class model for anomaly detection. Contribution 4 proposes a federated learning (FL) approach for anomaly detection in X-ray security imaging using OCSVM. The innovative machine learning approach can train a centralized model on data generated and located on multiple airports without compromising the privacy and security of the collected data. The performance of all novel methods in this these is evaluated in the context of Sydney airport dataset, synthetic data, and public datasets for X-ray images. Further, all the results of the novel methods are compared to the state-of-the-art methods. The experimental results shows that our proposed methods in the contributions outperform the state-of-the-art and produce promising results

Cost Factor Focused Scheduling and Sequencing: A Neoteric Literature Review

The hastily emergent concern from researchers in the application of scheduling and sequencing has urged the necessity for analysis of the latest research growth to construct a new outline. This paper focuses on the literature on cost minimization as a primary aim in scheduling problems represented with less significance as a whole in the past literature reviews. The purpose of this paper is to have an intensive study to clarify the development of cost-based scheduling and sequencing (CSS) by reviewing the work published over several parameters for improving the understanding in this field. Various parameters, such as scheduling models, algorithms, industries, journals, publishers, publication year, authors, countries, constraints, objectives, uncertainties, computational time, and programming languages and optimization software packages are considered. In this research, the literature review of CSS is done for thirteen years (2010-2022). Although CSS research originated in manufacturing, it has been observed that CSS research publications also addressed case studies based on health, transportation, railway, airport, steel, textile, education, ship, petrochemical, inspection, and construction projects. A detailed evaluation of the literature is followed by significant information found in the study, literature analysis, gaps identification, constraints of work done, and opportunities in future research for the researchers and experts from the industries in CSS

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Queue Formation Augmented with Particle Swarm Optimisation to Improve Waiting Time in Airport Security Screening

© 2019, Springer Nature Switzerland AG. Airport security screening processes are essential to ensure the safety of both passengers and the aviation industry. Security at airports has improved noticeably in recent years through the utilisation of state-of-the-art technologies and highly trained security officers. However, maintaining a high level of security can be costly to operate and implement. It may also lead to delays for passengers and airlines. This paper proposes a novel queue formation method based on a queueing theory model augmented with a particle swarm optimisation method known as QQT-PSO to improve the average waiting time in airport security areas. Extensive experiments were conducted using real-world datasets collected from Sydney airport. Compared to the existing system, our method significantly reduces the average waiting time and operating cost by 11.89% compared to the one-queue formation

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically