A general framework for statistical inference on discrete event systems.

We present a framework for statistical analysis of discrete event systems which combines tools such as simulation of marked point processes, likelihood methods, kernel density estimation and stochastic approximation to enable statistical analysis of the discrete event system, even if conventional approaches fail due to the mathematical intractability of the model.The approach is illustrated with an application to modelling and estimating corrosion of steel gates in the Dutch Haringvliet storm surge barrier.discrete event systems;kernel density estimation;optimization via simulation;parameter estimation;stochastic approximation;likelihood methods;market point process

Model-free reconstruction of neuronal network connectivity from calcium imaging signals

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically unfeasible even in dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct approximations to network structural connectivities from network activity monitored through calcium fluorescence imaging. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time-series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the effective network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (e.g., bursting or non-bursting). We thus demonstrate how conditioning with respect to the global mean activity improves the performance of our method. [...] Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good reconstruction of the network clustering coefficient, allowing to discriminate between weakly or strongly clustered topologies, whereas on the other hand an approach based on cross-correlations would invariantly detect artificially high levels of clustering. Finally, we present the applicability of our method to real recordings of in vitro cortical cultures. We demonstrate that these networks are characterized by an elevated level of clustering compared to a random graph (although not extreme) and by a markedly non-local connectivity.Comment: 54 pages, 8 figures (+9 supplementary figures), 1 table; submitted for publicatio

Fatigue Test and Prognosis Study of Welded Tubular Joints in Signal Support Structures

Steel welded tubular joints have been widely used in traffic signal support structures for economic and aesthetic reasons. However, they are susceptible to fatigue cracking which may lead to structural failure such as collapse, thus pose a threat to driver's safety. To address this issue, this study is focused on fatigue test, modeling and prognosis of the fatigue crack growth in full-scale welded tubular joints of traffic signal support structures. Fatigue test of six full-scale welded tubular joint specimens fabricated based on real design for signal support structure is conducted to obtain crack growth data. Details of test setup and results are reported in this dissertation. Two types of fatigue crack growth models are proposed for two regimes of fatigue crack development in welded tubular joints: the linear elastic fracture mechanics (LEFM) model for the slow crack growth regime (denoted as Stage II here) and the empirical failure model for the rapid crack growth regime (denoted as Stage III). Details of these two models including their mathematical expressions, stochastic parameters, sensitivity analysis and model application, are given in the dissertation. A sensor-driven structural health prognosis procedure that has an explicit stochastic measurement error term and thus can model the sensor performance degradation over monitoring period is proposed. The prognosis procedure involves the Bayesian theorem and Markov Chain Monte Carlo (MCMC) sampling for updating the structural degradation model using sensor data. An extreme value theory (EVT) based tail fitting method is proposed to reduce the heavy burden on data transmission and computing involved in sensor driven prognosis. This method employs moment estimator to calculate the small quantiles of the prognosis results by using a small portion of available sensor data. Finally, fatigue test data acquired in this study are used to examine the proposed fatigue life prognosis procedure. Both the LEFM based fatigue crack growth model and the empirical failure model are studied for fatigue life prognosis application. Prognosis results show that the prognosis procedure is able to provide good estimate of the fatigue crack growth curve of welded tubular joints in signal support structures if certain conditions are met