Simulating the Role of Transportation Infrastructure for Community Disaster Recovery

Functional recovery of transportation infrastructure after a disaster is essential for community disaster resilience as the recovery of damaged community components depends on their accessibility for repair. This paper presents a community disaster recovery simulation that accounts for community component's accessibility for repair using a demand-supply framework. Considered components of a community are viewed as suppliers and/or users of various resources and services essential for community functionality, reflected in components’ supply and demand properties. Whenever the demand of a component is not met, that component ceases to operate, simulating interdependency effects. Similarly, recovery demand is attributed to damaged components, representing the amounts of resources and services (e.g., workers, machinery and transportation services) these components need to recover. The proposed framework is illustrated on a virtual community with 3600 inhabitants supported by several interdependent infrastructure systems. The results show that the transportation network damage slows down the recovery of the virtual community by preventing access to damaged components and reducing the ability of the community to mobilize available repair resources. Furthermore, the effect of such prolonged transportation system recovery on the damage-free infrastructure systems whose functionality was decreased due to their dependency on the affected infrastructure systems, is quantified.

Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T

Three-dimensional (3D) encoding methods are increasingly being explored as alternatives to multi-slice two-dimensional (2D) acquisitions in fMRI, particularly in cases where high isotropic resolution is needed. 3D multi-shot EPI is the most popular 3D fMRI acquisition method, but is susceptible to physiological fluctuations which can induce inter-shot phase variations, and thus reducing the achievable tSNR, negating some of the benefit of 3D encoding. This issue can be particularly problematic at ultra-high fields like 7T, which have more severe off-resonance effects. In this work, we aim to improve the temporal stability of 3D multi-shot EPI at 7T by improving its robustness to inter-shot phase variations. We presented a 3D segmented CAIPI sampling trajectory ("seg-CAIPI") and an improved reconstruction method based on Hankel structured low-rank matrix recovery. Simulation and in-vivo results demonstrate that the combination of the seg-CAIPI sampling scheme and the proposed structured low-rank reconstruction is a promising way to effectively reduce the unwanted temporal variance induced by inter-shot physiological fluctuations, and thus improve the robustness of 3D multi-shot EPI for fMRI.

Improving low-voltage ride-through capability of a multimegawatt DFIG based wind turbine under grid faults

Large integration of doubly-fed induction generator (DFIG) based wind turbines (WTs) into power networks can have significant consequences for power system operation and the quality of the energy supplied due to their excessive sensitivity towards grid disturbances. Under voltage dips, the resulting overcurrent and overvoltage in the rotor circuit and the DC link of a DFIG, could lead to the activation of the protection system and WT disconnection. This potentially results in sudden loss of several tens/hundreds of MWs of energy, and consequently intensifying the severity of the fault. This paper aims to combine the use of a crowbar protection circuit and a robust backstepping control strategy that takes into consideration of the dynamics of the magnetic flux, to improve DFIG’s Low-Voltage Ride Through capability and fulfill the latest grid code requirements. While the power electronic interfaces are protected, the WTs also provide large reactive power during the fault to assist system voltage recovery. Simulation results using Matlab/Simulink demonstrate the effectiveness of the proposed strategy in terms of dynamic response and robustness against parametric variations.

Bayesian Analysis of Processed Information in Decision Making Experiments

In research on decision making, experiments are often analyzed in terms of decision strategies. These decision strategies define both which information is used as well as how it is used. However, often it is desirable to identify the used information without any further assumptions about how it is used. We provide a mathematical framework that allows analyzing which information is used by identifying consistent patterns on the choice probabilities. This framework makes it possible to generate the most general model consistent with an information usage hypothesis and then to test this model against others. We test our approach in a recovery simulation to show thatthe used information may be reliably identified AUC>= .90. In addition, to further verify the correctness we compare our approach with other approaches based on strategy fitting to show that both produce similar results.