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

MATHICSE Technical Report: Simulation-Based Anomaly Detection and Damage Localization: an Application to Structural Health Monitoring

We propose a simulation-based decision strategy for the proactive maintenance of complex structures with a particular application to structural health monitoring (SHM). The strategy is based on a data-driven approach which exploits an offine-online decomposition. A synthetic dataset is constructed offine by solving a parametric time-dependent partial differential equation for multiple input parameters, sampled from their probability distributions of natural variation. The collected time-signals, extracted at sensor locations, are used to train classiffiers at such sensor locations, thus constructing multiple databases of healthy configurations. These datasets are then used to train one class Support Vector Machines (OC-SVMs) to detect anomalies. During the online stage, a new measurement, possibly obtained from a damaged configuration, is evaluated using the classiffiers. Information on damage is provided in a hierarchical manner: first, using a binary feedback, the entire structure response is either classifiied as inlier (healthy) or outlier (damaged). Then, for the outliers, we exploit the outputs of multiple classiffiers to retrieve information both on the severity and the spatial location of the damages. Because of the large number of signals needed to construct the datasets offline, a model order reduction strategy is implemented to reduce the computational burden. We apply this strategy to both 2D and 3D problems to mimic the vibrational behavior of complex structures under the effect of an active source and show the effectiveness of the approach for detecting and localizing cracks

Bio-signal data gathering, management and analysis within a patient-centred health care context

The healthcare service is under pressure to do more with less, and changing the way the service is modelled could be the key to saving resources and increasing efficacy. This change could be possible using patient-centric care models. This model would include straightforward and easy-to-use telemonitoring devices and a flexible data management structure. The structure would maintain its state by ingesting many sources of data, then tracking this data through cleaning and processing into models and estimates to obtaining values from data which could be used by the patient. The system can become less disease-focused and more health-focused by being preventative in nature and allowing patients to be more proactive and involved in their care by automating the data management. This work presents the development of a new device and a data management and analysis system to utilise the data from this device and support data processing along with two examples of its use. These are signal quality and blood pressure estimation. This system could aid in the creation of patient-centric telecare systems

Crowd Scene Analysis in Video Surveillance

There is an increasing interest in crowd scene analysis in video surveillance due to the ubiquitously deployed video surveillance systems in public places with high density of objects amid the increasing concern on public security and safety. A comprehensive crowd scene analysis approach is required to not only be able to recognize crowd events and detect abnormal events, but also update the innate learning model in an online, real-time fashion. To this end, a set of approaches for Crowd Event Recognition (CER) and Abnormal Event Detection (AED) are developed in this thesis. To address the problem of curse of dimensionality, we propose a video manifold learning method for crowd event analysis. A novel feature descriptor is proposed to encode regional optical flow features of video frames, where adaptive quantization and binarization of the feature code are employed to improve the discriminant ability of crowd motion patterns. Using the feature code as input, a linear dimensionality reduction algorithm that preserves both the intrinsic spatial and temporal properties is proposed, where the generated low-dimensional video manifolds are conducted for CER and AED. Moreover, we introduce a framework for AED by integrating a novel incremental and decremental One-Class Support Vector Machine (OCSVM) with a sliding buffer. It not only updates the model in an online fashion with low computational cost, but also adapts to concept drift by discarding obsolete patterns. Furthermore, the framework has been improved by introducing Multiple Incremental and Decremental Learning (MIDL), kernel fusion, and multiple target tracking, which leads to more accurate and faster AED. In addition, we develop a framework for another video content analysis task, i.e., shot boundary detection. Specifically, instead of directly assessing the pairwise difference between consecutive frames over time, we propose to evaluate a divergence measure between two OCSVM classifiers trained on two successive frame sets, which is more robust to noise and gradual transitions such as fade-in and fade-out. To speed up the processing procedure, the two OCSVM classifiers are updated online by the MIDL proposed for AED. Extensive experiments on five benchmark datasets validate the effectiveness and efficiency of our approaches in comparison with the state of the art

Modeling and Intelligent Control for Spatial Processes and Spatially Distributed Systems

Dynamical systems are often characterized by their time-dependent evolution, named temporal dynamics. The space-dependent evolution of dynamical systems, named spatial dynamics, is another important domain of interest for many engineering applications. By studying both the spatial and temporal evolution, novel modeling and control applications may be developed for many industrial processes. One process of special interest is additive manufacturing, where a three-dimensional object is manufactured in a layer-wise fashion via a numerically controlled process. The material is printed over a spatial domain in each layer and subsequent layers are printed on top of each other. The spatial dynamics of the printing process over the layers is named the layer-to-layer spatial dynamics. Additive manufacturing provides great flexibility in terms of material selection and design geometry for modern manufacturing applications, and has been hailed as a cornerstone technology for smart manufacturing, or Industry 4.0, applications in industry. However, due to the issues in reliability and repeatability, the applicability of additive manufacturing in industry has been limited. Layer-to-layer spatial dynamics represent the dynamics of the printed part. Through the layer-to-layer spatial dynamics, it is possible to represent the physical properties of the part such as dimensional properties of each layer in the form of a heightmap over a spatial domain. Thus, by considering the spatial dynamics, it is possible to develop models and controllers for the physical properties of a printed part. This dissertation develops control-oriented models to characterize the spatial dynamics and layer-to-layer closed-loop controllers to improve the performance of the printed parts in the layer-to-layer spatial domain. In practice, additive manufacturing resources are often utilized as a fleet to improve the throughput and yield of a manufacturing system. An additive manufacturing fleet poses additional challenges in modeling, analysis, and control at a system-level. An additive manufacturing fleet is an instance of the more general class of spatially distributed systems, where the resources in the system (e.g., additive manufacturing machines, robots) are spatially distributed within the system. The goal is to efficiently model, analyze, and control spatially distributed systems by considering the system-level interactions of the resources. This dissertation develops a centralized system-level modeling and control framework for additive manufacturing fleets. Many monitoring and control applications rely on the availability of run-time, up-to-date representations of the physical resources (e.g., the spatial state of a process, connectivity and availability of resources in a fleet). Purpose-driven digital representations of the physical resources, known as digital twins, provide up-to-date digital representations of resources in run-time for analysis and control. This dissertation develops an extensible digital twin framework for cyber-physical manufacturing systems. The proposed digital twin framework is demonstrated through experimental case studies on abnormality detection, cyber-security, and spatial monitoring for additive manufacturing processes. The results and the contributions presented in this dissertation improve the performance and reliability of additive manufacturing processes and fleets for industrial applications, which in turn enables next-generation manufacturing systems with enhanced control and analysis capabilities through intelligent controllers and digital twins.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169635/1/baltaefe_1.pd

Path for Kernel Adaptive One-Class Support Vector Machine

International audienceThis paper proposes a Kernel Adaptive One Class SVM (KAOC-SVM) method based on the model introduced by A. Scholkopf and al. The aim is to find the solution path - the path of Lagrange multiplier a - as the kernel parameter changes from one value to another. It is similar to the regularization path approach proposed by Hastie and al., which finds the path when the regularization parameter ? changes from 0 to 1. In present case, the main difference is that the Lagrange multiplier paths are not piecewise linear anymore. Experimental results show that the proposed method is able to compute one-class SVMs with the same accuracy as traditional method but exploring all solutions combining 2 kernels. Simulation results are presented and CPU requirement is analyzed