A Framework for Group Modeling in Agent-Based Pedestrian Crowd Simulations

Pedestrian crowd simulation explores crowd behaviors in virtual environments. It is extensively studied in many areas, such as safety and civil engineering, transportation, social science, entertainment industry and so on. As a common phenomenon in pedestrian crowds, grouping can play important roles in crowd behaviors. To achieve more realistic simulations, it is important to support group modeling in crowd behaviors. Nevertheless, group modeling is still an open and challenging problem. The influence of groups on the dynamics of crowd movement has not been incorporated into most existing crowd models because of the complexity nature of social groups. This research develops a framework for group modeling in agent-based pedestrian crowd simulations. The framework includes multiple layers that support a systematic approach for modeling social groups in pedestrian crowd simulations. These layers include a simulation engine layer that provides efficient simulation engines to simulate the crowd model; a behavior-based agent modeling layers that supports developing agent models using the developed BehaviorSim simulation software; a group modeling layer that provides a well-defined way to model inter-group relationships and intra-group connections among pedestrian agents in a crowd; and finally a context modeling layer that allows users to incorporate various social and psychological models into the study of social groups in pedestrian crowd. Each layer utilizes the layer below it to fulfill its functionality, and together these layers provide an integrated framework for supporting group modeling in pedestrian crowd simulations. To our knowledge this work is the first one to focus on a systematic group modeling approach for pedestrian crowd simulations. This systematic modeling approach allows users to create social group simulation models in a well-defined way for studying the effect of social and psychological factors on crowd’s grouping behavior. To demonstrate the capability of the group modeling framework, we developed an application of dynamic grouping for pedestrian crowd simulations

Technology project investment analysis : a real options and game theoretic approach

This thesis develops a methology for evaluating technology investment decisions in an oligopolistic market structure. It integrates the game-theoretic models of strategies market interactions with a real options approach to investment under uncertainly and provides an improved understanding of the effects of uncertainty and competition on the strategic exercise of real options embedded in technology investments.Doctor of Philosophy (MPE

Correlating non-linear behavior of in-plane magnetic field and local domain wall velocities for quantitative stress evaluation

There is a need in industry to supply safe, effective and reliable technique to characterize the stress of steel components and structures, both at the manufacturing stage and in service. Bridging the correlation between micro and macro magnetic properties and the applied tensile stress is the first conceptual step to come up with a new method of non-destructive material testing. We investigate the stress-associated changes in domain wall dynamics in grain-oriented electrical steel by in-situ magnetic imaging using magneto-optical indicator films. The 180° domain walls velocity distribution is used as a parameter for applied stress determination. Additionally, the in-plane magnetic stray field above the surface of the sample is synchronously measured for stress evaluation. The variations in magnetic stray field outside the sample under different loading are investigated for the analysis of the domain wall dynamics. From this, an interrelation of the domain wall dynamics and magnetic stray fields with varied tensile stress is derived. The results provide substantial microscopic and macroscopic insight for the interplay of domain wall dynamics and stress-induced demagnetizing effect

Crack Evaluation Based on Novel Circle-Ferrite Induction Thermography

Nondestructive inspection of rolling contact and stress corrosion cracks is a critical important research area in both science and technology industry to evaluate the properties of a product. This paper proposes a novel system of circle-ferrite pulsed inductive thermography for cracks inspection. The new sensing structure consists of several promising characteristics. It significantly enhances the detectability of omnidirectional micro cracks, and provides larger detection area with non-geometry influence. In addition, unlike common inductor, the proposed structure imposes uniform toroidal electromagnetic thermal fields, so that both sensitivities of detection rate and the detectable area can be simultaneously improved. This overcomes the problem of in-homogenous heating, and increases the thermal contrast between directional defective and non-defective region. The theoretical derivation based on magnetic circuit principles has been developed for analysis and interpretation of the results. In addition, both simulation experiments and tests on artificial and nature cracks sample have been conducted to show the reliability and efficiency of the proposed system

Crack characterization in ferromagnetic steels by pulsed eddy current technique based on GA-BP neural network model

Ferromagnetic steels are widely used in engineering structures such as rail track, oil/gas pipeline and steel hanging bridge. Cracks resulted from manufacturing processes or previous loading will seriously undermine the safety of the engineering structures and even lead to catastrophic industrial accidents. Accurate and quantitative characterization the cracks in ferromagnetic steels are therefore of vital importance. In this paper, the cracks in ferromagnetic steels are detected by the pulsed eddy current (PEC) technique. Firstly, the physical mechanism of the relative magnetic permeability of the ferromagnetic steel on the detection signal of PEC is interpreted from a microscopic level of magnetic domain wall movement. The relationship of the crack width/depth and the detection signal of PEC is then investigated and verified by numerical simulations and experimental study. Finally, the cracks are inversely characterized by using Genetic Algorithm (GA) based Back-Propagation (BP) neural network (NN) considering the nonlinearity of the crack geometric parameters with the detection signal of PEC. The prediction results indicated that the proposed algorithm can characterize the crack depth and width within the relative error of 10%. The proposed approach combining PEC and GA based BPNN has been verified to quantitatively detect cracks in ferromagnetic steel