Parallel and distributed state estimation

The need in recent years for higher frequency in state estimation execution covering larger supervised networks has led to the investigation of faster and numerically more stable state estimation algorithms. However, technical developments in distributed Energy Management Systems, based on fast data communication networks, open up the possibility of parallel or distributed state estimation implementation. In this paper, this possibility is exploited to derive a solution methodology based on conventional state estimation algorithms and a coupling constraints optimization technique. Numerical experiments show suitable performance of the proposed method with regard to estimation accuracy, convergence robustness and computational efficiency. The results of these experiments also indicate the decoupled nature of the state estimation problem.published_or_final_versio

The influence of the electronic specific heat on swift heavy ion irradiation simulations of silicon

The swift heavy ion (SHI) irradiation of materials is often modelled using the two-temperature model. While the model has been successful in describing SHI damage in metals, it fails to account for the presence of a bandgap in semiconductors and insulators. Here we explore the potential to overcome this limitation by explicitly incorporating the influence of the bandgap in the parameterisation of the electronic specific heat for Si. The specific heat as a function of electronic temperature is calculated using finite temperature density functional theory with three different exchange correlation functionals, each with a characteristic bandgap. These electronic temperature dependent specific heats are employed with two-temperature molecular dynamics to model ion track creation in Si. The results obtained using a specific heat derived from density functional theory showed dramatically reduced defect creation compared to models that used the free electron gas specific heat. As a consequence, the track radii are smaller and in much better agreement with experimental observations. We also observe a correlation between the width of the band gap and the track radius, arising due to the variation in the temperature dependence of the electronic specific heat

Hidden Markov Models and their Application for Predicting Failure Events

We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates. Instead of a separate distribution for each hidden state, we use hierarchical mixtures of distributions in the exponential family. In our approach the observation distribution of the states is a finite mixture distribution of a small set of (simpler) distributions shared across all states. Using tied-mixture observation distributions offers several advantages. The mixtures act as a regularization for typically very sparse problems, and they reduce the computational effort for the learning algorithm since there are fewer distributions to be found. Using shared mixtures enables sharing of statistical strength between the Markov states and thus transfer learning. We determine for individual assets the trade-off between the risk of failure and extended operating hours by combining a MMMM with a partially observable Markov decision process (POMDP) to dynamically optimize the policy for when and how to maintain the asset.Comment: Will be published in the proceedings of ICCS 2020; @Booklet{EasyChair:3183, author = {Paul Hofmann and Zaid Tashman}, title = {Hidden Markov Models and their Application for Predicting Failure Events}, howpublished = {EasyChair Preprint no. 3183}, year = {EasyChair, 2020}

Changes in Dopamine Signalling Do Not Underlie Aberrant Hippocampal Plasticity in a Mouse Model of Huntington's Disease

Altered dopamine receptor labelling has been demonstrated in presymptomatic and symptomatic Huntington's disease (HD) gene carriers, indicating that alterations in dopaminergic signalling are an early event in HD. We have previously described early alterations in synaptic transmission and plasticity in both the cortex and hippocampus of the R6/1 mouse model of Huntington's disease. Deficits in cortical synaptic plasticity were associated with altered dopaminergic signalling and could be reversed by D1- or D2-like dopamine receptor activation. In light of these findings we here investigated whether defects in dopamine signalling could also contribute to the marked alteration in hippocampal synaptic function. To this end we performed dopamine receptor labelling and pharmacology in the R6/1 hippocampus and report a marked, age-dependent elevation of hippocampal D1 and D2 receptor labelling in R6/1 hippocampal subfields. Yet, pharmacological inhibition or activation of D1- or D2-like receptors did not modify the aberrant synaptic plasticity observed in R6/1 mice. These findings demonstrate that global perturbations to dopamine receptor expression do occur in HD transgenic mice, similarly in HD gene carriers and patients. However, the direction of change and the lack of effect of dopaminergic pharmacological agents on synaptic function demonstrate that the perturbations are heterogeneous and region-specific, a finding that may explain the mixed results of dopamine therapy in HD