Protection des Infrastructures Essentielles par Advanced Modélisation, simulation et optimisation pour l’atténuation et résilience de défaillance en cascade

Continuously increasing complexity and interconnectedness of modern critical infrastructures, together with increasingly complex risk environments, pose unique challenges for their secure, reliable, and efficient operation. The focus of the present dissertation is on the modelling, simulation and optimization of critical infrastructures (CIs) (e.g., power transmission networks) with respect to their vulnerability and resilience to cascading failures. This study approaches the problem by firstly modelling CIs at a fundamental level, by focusing on network topology and physical flow patterns within the CIs. A hierarchical network modelling technique is introduced for the management of system complexity. Within these modelling frameworks, advanced optimization techniques (e.g., non-dominated sorting binary differential evolution (NSBDE) algorithm) are utilized to maximize both the robustness and resilience (recovery capacity) of CIs against cascading failures. Specifically, the first problem is taken from a holistic system design perspective, i.e. some system properties, such as its topology and link capacities, are redesigned in an optimal way in order to enhance system’s capacity of resisting to systemic failures. Both topological and physical cascading failure models are applied and their corresponding results are compared. With respect to the second problem, a novel framework is proposed for optimally selecting proper recovery actions in order to maximize the capacity of the CI network of recovery from a disruptive event. A heuristic, computationally cheap optimization algorithm is proposed for the solution of the problem, by integrating foundemental concepts from network flows and project scheduling. Examples of analysis are carried out by referring to several realistic CI systems.Sans cesse croissante complexité et l'interdépendance des infrastructures critiques modernes, avec des environs de risque plus en plus complexes, posent des défis uniques pour leur exploitation sûre, fiable et efficace. L'objectif de la présente thèse est sur la modélisation, la simulation et l'optimisation des infrastructures critiques (par exemple, les réseaux de transmission de puissance) à l'égard de leur vulnérabilité et la résilience aux défaillances en cascade. Cette étude aborde le problème en modélisant infrastructures critiques à un niveau fondamental, en se concentrant sur la topologie du réseau et des modèles de flux physiques dans les infrastructures critiques. Un cadre de modélisation hiérarchique est introduit pour la gestion de la complexité du système. Au sein de ces cadres de modélisation, les techniques d'optimisation avancées (par exemple, non-dominée de tri binaire évolution différentielle (NSBDE) algorithme) sont utilisés pour maximiser à la fois la robustesse et la résilience (capacité de récupération) des infrastructures critiques contre les défaillances en cascade. Plus précisément, le premier problème est pris à partir d'un point de vue de la conception du système holistique, c'est-à-dire certaines propriétés du système, tels que ses capacités de topologie et de liaison, sont redessiné de manière optimale afin d'améliorer la capacité de résister à des défaillances systémiques de système. Les deux modèles de défaillance en cascade topologiques et physiques sont appliquées et leurs résultats correspondants sont comparés. En ce qui concerne le deuxième problème, un nouveau cadre est proposé pour la sélection optimale des mesures appropriées de récupération afin de maximiser la capacité du réseau d’infrastructure critique de récupération à partir d'un événement perturbateur. Un algorithme d'optimisation de calcul pas cher heuristique est proposé pour la solution du problème, en intégrant des concepts fondamentaux de flux de réseau et le calendrier du projet. Exemples d'analyse sont effectués en se référant à plusieurs systèmes de CI réalistes

Graph-based ship traffic partitioning for intelligent maritime surveillance in complex port waters

Maritime Situational Awareness (MSA) is a critical component of intelligent maritime traffic surveillance. However, it becomes increasingly challenging to gain MSA accurately given the growing complexity of ship traffic patterns due to multi-ship interactions possibly involving classical manned ships and emerging autonomous ships. This study proposes a new traffic partitioning methodology to realise the optimal maritime traffic partition in complex waters. The methodology combines conflict criticality and spatial distance to generate conflict-connected and spatially compact traffic clusters, thereby improving the interpretability of traffic patterns and supporting ship anti-collision risk management. First, a composite similarity measure is designed using a probabilistic conflict detection approach and a newly formulated maritime traffic route network learned through maritime knowledge mining. Then, an extended graph-based clustering framework is used to produce balanced traffic clusters with high intra-connections but low inter-connections. The proposed methodology is thoroughly demonstrated and tested using Automatic Identification System (AIS) trajectory data in the Ningbo-Zhoushan Port. The experimental results show that the proposed methodology 1) has effective performance in decomposing the traffic complexity, 2) can assist in identifying high-risk/density traffic clusters, and 3) is sufficiently generic to handle various traffic scenarios in complex geographical waters. Therefore, this study makes significant contributions to intelligent maritime surveillance and provides a theoretical foundation for promoting maritime anti-collision risk management for the future mixed traffic of both manned and autonomous ships

Quantifying joint behavioral states in zebrafish (Danio rerio) dyadic contests through interpretable variables

Tese de Mestrado, Engenharia Biomédica e Biofísica, 2021, Universidade de Lisboa, Faculdade de CiênciasO comportamento animal é uma área fascinante do ponto de vista físico, no entanto ainda existem vários desafios associados à construção de modelos ou ao desenvolvimento de teorias do comportamento em física. Um dos desafios é desenvolver modelos diretamente dos dados, eliminando o viés antropocêntrico que existe na definição de estados comportamentais. Um bom exemplo da complexidade associada ao comportamento pode ser encontrado em interações sociais, nomeadamente interações agonistas entre peixes-zebra (Danio rerio). Estas interações são bem compreendidas e estereotípicas, e existem catálogos a descrever os estados comportamentais associados a cada fase da interação. Isto e a versatilidade genética a que o peixe zebra se encontra associado, tornam esta interação ideal para o nosso estudo. O nosso objetivo principal consiste na tentativa de derivar um conjunto de estados comportamentais diretamente a partir dos dados experimentais obtidos, sendo estes estados definidos para o conjunto, e não individualmente. Fazemos isso sob a assunção de em interações sociais, estados comportamentais dependem dos elementos envolvidos nessa interação (neste caso, são peixes-zebra) e que esta não é completamente descrita, exceto se levar ambos em conta simultaneamente. Os dados são esqueletos tridimensionais dos 2 peixes-zebra num volume. O processo de aquisição desses dados consiste na aquisição de imagens em 3 planos bidimensionais com câmaras de alta definição, e um pipeline de processamento, que combina várias redes neuronais para a identificação de pontos corporais, a atribuição de identidade temporal aos peixes envolvidos, e a interpolação das imagens nos diferentes planos. Este processo permite a conversão de vários vídeos em sinais temporais que podem ser manipulados e processados de forma adequada. Usamos variáveis interpretáveis, no caso, a distância, os alinhamentos de direcção e aceleração, e os ritmos de batimento de cauda. Essas variáveis embora sejam simples, podem dizer bastante informação sobre a natureza do comportamento, sendo úteis numa exploração inicial. Definimos estados comportamentais compostos (colecção de vários comportamentos efectuados pelos peixes ao longo de um determinado período de tempo) e exploramos a dinâmica de uma luta nesta descrição simplificada. O sistema que resulta das variáveis definidas possui 6 dimensões, projectamos esse sistema para um plano bidimensional para melhor análise. Efectua-se um histograma das novas variáveis, e ter uma estimativa da densidade de probabilidade através da convolução do mesmo com uma gaussiana bidimensional. Detecta-se os picos de densidade, que neste sistema podem ser interpretados como estados comportamentais. Com essa descrição é possível gerar uma sequência simbólica que representa a dinâmica da interacção como sendo a transição entre vários estados comportamentais discretos. Constrói-se uma matriz que representa a transição entre os vários estados, e por decomposição espectral pode-se observar o comportamento dos valores próprios em função do número de transições e é possível decompor os estados em vários conjuntos através dos vectores próprios, cuja dinâmica entre eles é representada pelo valor próprio associado. Através da sequência simbólica é possível uma descrição da interacção entre os elementos, tendo inclusive informação sobre a escala temporal associada à dinâmica entre esses estados. Ao associar os clusters aos diferentes estados comportamentais compostos definidos previamente, é possível ver que certos clusters se encontram associados, e apresenta uma certa estrutura, que pode ser representativa da dinâmica real. Também é possível determinar a escala temporal de interações entre diferentes conjuntos de clusters. Foi possível determinar que os comportamentos ocorrem em escalas temporais maiores do que a escala típica para processo de Markov, e a escala temporal mais elevada se encontra associada a transição entre estados associados à agressão entre o par, e estados associados aos períodos entre lutas. Mostramos que é possível obter uma estrutura comportamental da luta entre dois peixes-zebra utilizando as variáveis simples que definimos. Isto é um framework que permite explorar a dinâmica da sua interação em maior detalhe, a utilizar variáveis ou representações mais precisas, que podem não ser interpretáveis

Applying complexity science to air traffic management

Complexity science is the multidisciplinary study of complex systems. Its marked network orientation lends itself well to transport contexts. Key features of complexity science are introduced and defined, with a specific focus on the application to air traffic management. An overview of complex network theory is presented, with examples of its corresponding metrics and multiple scales. Complexity science is starting to make important contributions to performance assessment and system design: selected, applied air traffic management case studies are explored. The important contexts of uncertainty, resilience and emergent behaviour are discussed, with future research priorities summarised

Improved coordinated automatic voltage control in power grids through complex network analysis

PhD ThesisAutomatic and Co-ordinated Voltage Regulation (CVR) contributes significantly to economy and security of transmission grids. The role of CVR will become more critical as systems are operated closer to their capacity limits due to technical, economic and environmental reasons. CVR has 1 min resolution and owing to the inherent complexity of the task, CVR is enabled through zoning-based Reduced Control Models (RCM) i.e. simplified models of the network suitable for Voltage Control. RCM not only enables CVR bus also affects its performance and robustness. This thesis contributes towards improved CVR through thorough investigation of the RCM. As a starting point, with current power systems structure in mind, this work investigates static RCM schemes (i.e. fixed Reduced Control Model for all network configurations). To that end this thesis develops: (1) a novel generic framework for CVR modelling and evaluation and (2) new zoning-based RCM approaches using Complex Network Analysis. The evaluation of CVR in conjunction with both static and adaptive RCM schemes are based on a novel framework for CVR modelling and evaluation. This framework is generic and can be used to facilitate the selection and design of any of the CVR components. As a next step, due to the fact that a single RCM cannot be optimal for all network configurations, adaptive RCM (i.e. RCM determined in an online event driven fashion) is investigated using the proposed framework. This concerns future transmission grids where RCM is driven by the need for reliability rather than economy of measurement points at a planning phase. Lastly, this thesis examines zone division in an interconnected system ranging from EHV down to MV, and assesses the required degree of co-ordination for the voltage control of these zones. Essentially, this last item extends the scope of this work’s contributions beyond a single transmission-level Independent System Operator (ISO).EPSRC for funding my Research and the Consortium of the “Autonomic Power System” project

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

MODELLING AND SYSTEMATIC EVALUATION OF MARITIME TRAFFIC SITUATION IN COMPLEX WATERS

Maritime Situational Awareness (MSA) plays a vital role in the development of intelligent transportation support systems. The surge in maritime traffic, combined with increasing vessel sizes and speeds, has intensified the complexity and risk of maritime traffic. This escalation presents a considerable challenge to the current systems and tools dedicated to maritime traffic monitoring and management. Meanwhile, the existing literature on advanced MSA methods and techniques is relatively limited, especially when it comes to addressing multi-ship interactions that may involve hybrid traffic of manned ships and emerging autonomous ships in complex and restricted waters in the future. The primary research question revolves around the challenge faced by current collision risk models in incorporating the impact of traffic characteristics in complex waters. This limitation hampers their effectiveness in managing complex maritime traffic situations. In view of this, the research aims to investigate and analyse the traffic characteristics in complex port waters and develop a set of advanced MSA methods and models in a holistic manner, so as to enhance maritime traffic situation perception capabilities and strengthen decision-making on anti-collision risk control. This study starts with probabilistic conflict detection by incorporating the dynamics and uncertainty that may be involved in ship movements. Then, the conflict criticality and spatial distance indicators are used together to partition the regional ship traffic into several compact, scalable, and interpretable clusters from both static and dynamic perspectives. On this basis, a systematic multi-scale collision risk approach is newly proposed to estimate the collision risk of a given traffic scenario from different spatial scales. The novelty of this research lies not only in the development of new modelling techniques on MSA that have never been done by using various advanced techniques (e.g., Monte Carlo simulation, image processing techniques, graph-based clustering techniques, complex network theory, and fuzzy clustering iterative method) but also in the consideration of the impact of traffic characteristics in complex waters, such as multi-dependent conflicts, restricted water topography, and dynamic and uncertain ship motion behaviours. Extensive numerical experiments based on real AIS data in the world's busiest and most complex water area (i.e., Ningbo_Zhoushan Port, China) are carried out to evaluate the models’ performance. The research results show that the proposed models have rational and reliable performance in detecting potential collision danger under an uncertain environment, identifying high-risk traffic clusters, offering a complete comprehension of a traffic situation, and supporting strategic maritime safety management. These developed techniques and models provide useful insights and valuable implications for maritime practitioners on traffic surveillance and management, benefiting the safety and efficiency enhancement of maritime transportation. The research can also be tailored for a wide range of applications given its generalization ability in tackling various traffic scenarios in complex waters. It is believed that this work would make significant contributions in terms of 1) improving traffic safety management from an operational perspective without high financial requirements on infrastructure updating and 2) effectively supporting intelligent maritime surveillance and serving as a theoretical basis of promoting maritime safety management for the complex traffic of mixed manned and autonomous ships

Multi-modal and multi-model interrogation of large-scale functional brain networks

Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours