Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems

Modern urban railways extensively use computerized sensing and control technologies to achieve safe, reliable, and well-timed operations. However, the use of these technologies may provide a convenient leverage to cyber-attackers who have bypassed the air gaps and aim at causing safety incidents and service disruptions. In this paper, we study false data injection (FDI) attacks against railways' traction power systems (TPSes). Specifically, we analyze two types of FDI attacks on the train-borne voltage, current, and position sensor measurements - which we call efficiency attack and safety attack -- that (i) maximize the system's total power consumption and (ii) mislead trains' local voltages to exceed given safety-critical thresholds, respectively. To counteract, we develop a global attack detection (GAD) system that serializes a bad data detector and a novel secondary attack detector designed based on unique TPS characteristics. With intact position data of trains, our detection system can effectively detect the FDI attacks on trains' voltage and current measurements even if the attacker has full and accurate knowledge of the TPS, attack detection, and real-time system state. In particular, the GAD system features an adaptive mechanism that ensures low false positive and negative rates in detecting the attacks under noisy system measurements. Extensive simulations driven by realistic running profiles of trains verify that a TPS setup is vulnerable to the FDI attacks, but these attacks can be detected effectively by the proposed GAD while ensuring a low false positive rate.Comment: IEEE/IFIP DSN-2016 and ACM Trans. on Cyber-Physical System

Population-based simulation optimization for urban mass rapid transit networks

In this paper, we present a simulation-based headway optimization for urban mass rapid transit networks. The underlying discrete event simulation model contains several stochastic elements, including time-dependent demand and turning maneuver times as well as direction-dependent vehicle travel and passenger transfer times. Passenger creation is a Poisson process that uses hourly origin–destination-matrices based on anonymous mobile phone and infrared count data. The numbers of passengers on platforms and within vehicles are subject to capacity restrictions. As a microscopic element, passenger distribution along platforms and within vehicles is considered. The bi-objective problem, involving cost reduction and service level improvement, is transformed into a single-objective optimization problem by normalization and scalarization. Population-based evolutionary algorithms and different solution encoding variants are applied. Computational experience is gained from test instances based on real-world data (i.e., the Viennese subway network). A covariance matrix adaptation evolution strategy performs best in most cases, and a newly developed encoding helps accelerate the optimization process by producing better short-term results. Document type: Articl

Systematic Parameter Optimization and Application of Automated Tracking in Pedestrian-Dominant Situations

RÉSUMÉ Les mouvements des piétons et leur modélisation constituent un domaine de recherche de plus en plus actif. Bien qu’encore souvent appliqué à la sécurité par l’élaboration de plans d’évacuation en cas d’urgence, comprendre le mouvement des piétons est un enjeu économique de plus en plus important, notamment pour améliorer l’efficacité des aménagements de transport et des grands centres commerciaux. Cependant, les données existantes — particulièrement au niveau individuel, ou microscopique —sont majoritairement collectées dans des situations expérimentales contrôlées. Elles ne sont donc pas nécessairement représentatives du comportement des piétons dans des situations réelles, particulièrement en tenant compte de la susceptibilité de leur comportement aux facteurs démographiques, psychologiques et nvironnementaux. Cette lacune est due principalement à l’absence de méthodes prouvées pour la détection et le suivi de piétons dans des cas réels, absence qui résulte de la complexité des mouvements piétons et qui persiste malgré l’avancement continu des méthodes automatique d’analyse.----------ABSTRACT Though a wealth of data exists for the characterization of pedestrian movement, a majority of it originates from experimental settings owing to the current state of trackers for real-world scenarios. While these trackers are steadily improving, they remain insufficiently reliable for the accurate, microscopic tracking of individuals, particularly in cases of occlusion or higher density, complex scenes. In this work, the use of evolution algorithms is proposed for the systematic calibration of the parameters of existing trackers in order to further optimize their performance – evaluated by tracking accuracy and precision metrics – in complex cases, with an initial focus on two tracking methods designed for multimodal analysis. This calibration is further aided by the inclusion of additional parameters regulating homography, or specifically the plane to which tracker detections are projected. Three real test cases were used: a) a confined corridor in a public building, b) a subway station entrance during morning rush hour and c) a crosswalk in downtown New York. Results demonstrate a halving of tracking errors over both default and manually-calibrated parameters, as well as a strong correlation in performance between similar cases. These results were consistent over multiple trials and regardless of the starting parameters, strongly implying that the obtained solutions are indeed the global maxima for each scene. For application and validation of the resultant tracks, flow characterization and directional counting are demonstrated, utilizing tools included in the optimization framework

The Performance of Biomimicry Architecture in Sustainable Design for a Mixed-Use Workplace in Shanghai (Sustainable Design)

Abstract Sustainable architecture design is becoming more and more popular all over the world, especially in China. Active sustainable strategies play an important role in sustainable architecture design such as solar panels, wind turbines, and roof gardens. However, this Thesis will find some new passive ways to improve the sustainability of buildings by proving bionic technology. The thesis seeks to integrate living organisms into buildings to improve the sustainability of buildings and generate sustainable resources. This main focus is biomimetics. The technology used in the design of architecture sustainability. Bionics or biomimicry refers to artificial processes or systems that mimic nature. The thesis will develop a program that is about how to interpret biomimicry language to architecture language and apply it to the design of a building to improve its performance. The thesis finally mainly use three biomimicry technology to design the building. They are respectively (1) a termite mound structure to advance ventilation of the building, (2)algae to clean carbon dioxide, and (3) a three-leaf clover floor plan layout and building form. to create more fresh energy for the building. In addition, the thesis aims to use more biomimicry solutions to overcome those problems from site analysis

Reacting plume inversion on urban geometries through gradient based design methodologies

An increased focus on domestic security in recent years has brought attention to several important application areas where computational fluid dynamics (CFD) has the ability to make a significant impact. In particular, disaster mitigation and post-event forensic activities are of interest. This work investigates a procedure built on gradient based design methods to allow for the solution of the so-called inverse chemistry problem in urban environments. The inverse chemistry problem consists of computing a release location based on the sensing of chemical byproducts of the release and the ability to compute an accurate flow field on the geometry of interest. In this study, Washington DC is simulated under conditions of a hazardous plume. A CFD solver is implemented which allows for the solution of the preconditioned finite-rate Navier-Stokes equations as well as the in situ computation of design gradients

Numerical Computation, Data Analysis and Software in Mathematics and Engineering

The present book contains 14 articles that were accepted for publication in the Special Issue “Numerical Computation, Data Analysis and Software in Mathematics and Engineering” of the MDPI journal Mathematics. The topics of these articles include the aspects of the meshless method, numerical simulation, mathematical models, deep learning and data analysis. Meshless methods, such as the improved element-free Galerkin method, the dimension-splitting, interpolating, moving, least-squares method, the dimension-splitting, generalized, interpolating, element-free Galerkin method and the improved interpolating, complex variable, element-free Galerkin method, are presented. Some complicated problems, such as tge cold roll-forming process, ceramsite compound insulation block, crack propagation and heavy-haul railway tunnel with defects, are numerically analyzed. Mathematical models, such as the lattice hydrodynamic model, extended car-following model and smart helmet-based PLS-BPNN error compensation model, are proposed. The use of the deep learning approach to predict the mechanical properties of single-network hydrogel is presented, and data analysis for land leasing is discussed. This book will be interesting and useful for those working in the meshless method, numerical simulation, mathematical model, deep learning and data analysis fields

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Computationally efficient simulation in urban mechanised tunnelling based on multi-level BIM models

The design of complex underground infrastructure projects involves various empirical, analytical or numerical models with different levels of complexity. The use of simulation models in current state-of-the-art tunnel design process can be cumbersome when significant manual, time-consuming preparation, analysis and excessive computing resources are required. This paper addresses the challenges connected with minimising the user workload and computational time, as well as enabling real-time computations during the construction. To ensure a seamless workflow during design and to minimise the computation time of the analysis, we propose a novel concept for BIM-based numerical simulations, enabling the modelling of the tunnel advance on different levels of detail in terms of geometrical representation, material modelling and modelling of the advancement process. To ensure computational efficiency, the simulation software has been developed with special emphasis on efficient implementation, including parallelisation strategies on shared and distributed memory systems. For real-time on-demand calculations, simulation based meta models are integrated into the software platform. The components of the BIM-based multi-level simulation concept are described and evaluated in detail by means of representative numerical examples