Developed within the framework of the ESTIA research and innovation project, the ESTIA platform is a versatile technological solution that allows the prediction, detection and management of incidents that are related with the risk of structural fires within cultural heritage (CH) settlements and sites. The ESTIA platform is a distributed system that consists of collaborating autonomous subsystems, ensuring a broad range of applications through the platform’s potential to adapt to each deployment’s specific needs and according to the requirements of the targeted end-users.
By incorporating advanced procedures for the semi-automatic digitization of the CH built environment as well as an advanced system that simulates the development of the complex phenomena of fire propagation and human crowd behaviour, the platform is an effective tool assisting competent authorities in assessing the fire-related risks and offering training to first responders and field officers. Additionally, the platform offers an effective fire incident management system that includes fire-detection capabilities and a specialised decision support system, enhancing authorities during the management of a developing fire incident.
The validation of the ESTIA solution against the requirements of its distinct use cases was performed including the use of the platform as a simulation-based fire risk assessment tool. Subsequently, an exploratory risk assessment study was conducted aiming to establish and demonstrate a methodology for performing risk assessment studies using the provided technological solution.
This paper presents (i) the use of the ESTIA platform as a tool for the conduction of simulation-based assessment of risk related to fire incidents within a CH environment, (ii) the methodology for the technological validation of the ESTIA platform as a simulation-based fire risk assessment tool and (iii) the methodology for the conduction of the exploratory risk assessment study in the historical center of Xanthi (Greece).
KEYWORDS: Biometrics, 3D modeling, Information fusion, Computer simulations, Databases, Inspection, Telecommunications, Visualization, Systems modeling, Algorithm development
Multi-biometrics have long been considered as a means of providing an irrefutable identification of a person by providing the necessary complementarity of the different modalities involved in reducing false positives and increasing correct identifications. Furthermore, non-contact biometrics have been considered essential in achieving identification on-the-fly without imposing unnecessary delays and inconvenience when checking one’s ID. In the context of D4FLY, an EU-funded project under Horizon 2020, a corridor-like multi-biometric layout has been developed to allow noncontact identification on-the-go. The iCrowd crowd behavior simulator has been used to test the operational performance of the biometric corridor configuration in terms of throughput, delays and service times. This paper reports the quantitative performance results of the D4FLY biometric corridor.
KEYWORDS: C2I, 3D modeling, Visualization, Computer simulations, Device simulation, Data modeling, Control systems, Information security, Cameras, Telecommunications
The combination of digital twins and simulation, alongside with a control, command, and information system, provides a powerful hybrid environment for operational testing and performance assessment of security systems under realistic conditions without interrupting the operation of the test environment. This paper summarizes the use of OCUSIM, a hybrid Control, Command & Information (C2I) and simulation environment, and the associated requirements for 3D modeling, simulation and data exchange in cyber-physical threat assessment, multi-biometrics performance evaluation, and risk-based access control in different security environments. OCUSIM is based on the integration of the OCULUS C2I system with the iCrowd simulation environment along the lines of the digital twin concept. Other use cases and different application domains for OCUSIM are also discussed.
The SAFETY4RAILS2 project delivers methods and systems to increase the safety and recovery of track-based inter-city railway and intra-city metro transportation. When an incident occurs during heavy usage, metro and railway operators have to consider many aspects to ensure passenger safety and security. The EU funded project SAFETY4RAILS, aims to improve the handling of such events through a holistic approach by combining a wide range of analytic tools to detect, prevent, mitigate and respond to cyber-physical attacks to railway networks. In the context of assessing the impact on the crowd inside the rail/metro station and its surroundings from a cyberattack against a rail/metro infrastructure and evaluating the effectiveness of mitigation measures in case of an attack, the iCrowd simulation platform is used in conjunction with external modules that simulate the cyber-physical attacks. This paper reports the results and lessons learnt from these simulations and provides an insight on mitigation measures that may be necessary to reduce infrastructure vulnerabilities under different cyber-physical attack scenarios to several different rail/metro infrastructures.
The iCrowd human and crowd behavior simulator provides an integrated simulation platform for simulating crowd behavior alongside with simulation of physical phenomena and their interaction with and impact on the behavior of humans, including cognitive and psychological aspects and information exchange. The iCrowd simulation platform has been applied to various complex scenarios including evacuation of people from buildings and outdoor environments in case of fire, simulation and testing of risk-based security strategies and protocols, anomaly detection in security-sensitive environments based on human tracks, and others. Recently, iCrowd has become available in the form of a Simulation-as-a-Service (SaaS) environment implemented on virtual machines (VMs) and remote access through secure HTTPS connections. The SaaS iCrowd environment has been tested by qualified end-users in designing and evaluating the performance of risk-based security strategies for border crossing, evaluating the performance of novel biometrics, and for crowd evacuation in an urban environment on the scale of mid-size town with photorealistic modeling of the simulated environment. The feedback from the use of the iCrowd SaaS environment by end-users has been very encouraging and limited training was required to get end-users familiarized in the use of the simulator. In this paper we report on the different use cases the iCrowd SaaS was used to train different end-user groups and on the evaluation results from the training of these groups in terms of learning and performance objectives.
Risk-based and automatic security systems require to monitor passengers’ whereabouts in a terminal discretely to allow timely detection of suspicious behaviors and preventing malicious actions. In a series of two papers Thomopoulos et al. have introduced a methodology providing real-time risk assessment for airport passengers based on their trajectories. The proposed methodology implements a deep learning architecture. It is fully automated, reducing the workload of the video surveillance operators leading to less error-prone conclusions. Furthermore, the proposed methodology has been integrated with the OCULUS Command & Control (C2) System and the i-Crowd Simulator, a crowd simulation platform developed in the Integrated Systems Lab (ISL) of the Institute of Informatics and Telecommunications at NCSR “Demokritos.” In this paper we extend our previous work by introducing noise in both training and testing data used for tracking passengers and detecting anomalies in their tracks. Extensive testing of the anomaly detection system in the presence of noise demonstrates that the system is extremely resilient in noise. Furthermore, we consider the case of missing data in both training and testing data in order to model a realistic scenario of tracking with cameras with gaps in the passengers tracks from camera to camera due to missing data from transmission delays and/or data overflow. Extensive testing with the i- Crowd simulator demonstrates considerable robustness in the performance of the anomaly detection system in both noisy and missing data. The experimental results indicate that the proposed anomaly detection system is robust to both noisy and missing data and thus a very promising risk assessment scheme that can reliably be used for risk-based security under realistic operational conditions.
Geocoding information in the 2D or 3D space in a systematic and dynamic way is a challenging problem. Furthermore, indoor navigating around the content in the physical or virtual space is also a demanding problem. wayGoo is a 2D/3D geocoding platform that offers dynamic geocoding and navigation in both physical and virtual spaces. In a security application, one would like to connect the dynamic geocoding capabilities with control and command functionalities required in monitoring and managing security environments. Integrating wayGoo with the OCULUS C2I system provides exactly this capability for dynamically monitoring, interacting and managing security spaces. In this paper, we present the integrated wayGoo OCULUS C2I environment and demonstrate its functionalities through several use case scenarios.
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