The vibro-acoustic analysis of built-up systems using a hybrid method with parametric and non-parametric uncertainties

An existing hybrid finite element (FE)/statistical energy analysis (SEA) approach to the analysis of the mid- and high frequency vibrations of a complex built-up system is extended here to a wider class of uncertainty modeling. In the original approach, the constituent parts of the system are considered to be either deterministic, and modeled using FE, or highly random, and modeled using SEA. A non-parametric model of randomness is employed in the SEA components, based on diffuse wave theory and the Gaussian Orthogonal Ensemble (GOE), and this enables the mean and variance of second order quantities such as vibrational energy and response cross-spectra to be predicted. In the present work the assumption that the FE components are deterministic is relaxed by the introduction of a parametric model of uncertainty in these components. The parametric uncertainty may be modeled either probabilistically, or by using a non-probabilistic approach such as interval analysis, and it is shown how these descriptions can be combined with the non-parametric uncertainty in the SEA subsystems to yield an overall assessment of the performance of the system. The method is illustrated by application to an example built-up plate system which has random properties, and benchmark comparisons are made with full Monte Carlo simulations. © 2012 Elsevier Ltd. All rights reserved.

A Hierarchical Model for Ordinal Matrix Factorization

This paper proposes a hierarchical probabilistic model for ordinal matrix factorization. Unlike previous approaches, we model the ordinal nature of the data and take a principled approach to incorporating priors for the hidden variables. Two algorithms are presented for inference, one based on Gibbs sampling and one based on variational Bayes. Importantly, these algorithms may be implemented in the factorization of very large matrices with missing entries. The model is evaluated on a collaborative filtering task, where users have rated a collection of movies and the system is asked to predict their ratings for other movies. The Netflix data set is used for evaluation, which consists of around 100 million ratings. Using root mean-squared error (RMSE) as an evaluation metric, results show that the suggested model outperforms alternative factorization techniques. Results also show how Gibbs sampling outperforms variational Bayes on this task, despite the large number of ratings and model parameters. Matlab implementations of the proposed algorithms are available from cogsys.imm.dtu.dk/ordinalmatrixfactorization.

The role of human orbitofrontal cortex in value comparison for incommensurable objects.

The human orbitofrontal cortex is strongly implicated in appetitive valuation. Whether its role extends to support comparative valuation necessary to explain probabilistic choice patterns for incommensurable goods is unknown. Using a binary choice paradigm, we derived the subjective values of different bundles of goods, under conditions of both gain and loss. We demonstrate that orbitofrontal activation reflects the difference in subjective value between available options, an effect evident across valuation for both gains and losses. In contrast, activation in dorsal striatum and supplementary motor areas reflects subjects' choice probabilities. These findings indicate that orbitofrontal cortex plays a pivotal role in valuation for incommensurable goods, a critical component process in human decision making.

Confidence in beliefs about pain predicts expectancy effects on pain perception and anticipatory processing in right anterior insula.

Psychological factors play a major role in exacerbating chronic pain. Effective self-management of pain is often hindered by inaccurate beliefs about the nature of pain which lead to a high degree of emotional reactivity. Probabilistic models of perception state that greater confidence (certainty) in beliefs increases their influence on perception and behavior. In this study, we treat confidence as a metacognitive process dissociable from the content of belief. We hypothesized that confidence is associated with anticipatory activation of areas of the pain matrix involved with top-down modulation of pain. Healthy volunteers rated their beliefs about the emotional distress that experimental pain would cause, and separately rated their level of confidence in this belief. Confidence predicted the influence of anticipation cues on experienced pain. We measured brain activity during anticipation of pain using high-density EEG and used electromagnetic tomography to determine neural substrates of this effect. Confidence correlated with activity in right anterior insula, posterior midcingulate and inferior parietal cortices during the anticipation of pain. Activity in the right anterior insula predicted a greater influence of anticipation cues on pain perception, whereas activity in right inferior parietal cortex predicted a decreased influence of anticipatory cues. The results support probabilistic models of pain perception and suggest that confidence in beliefs is an important determinant of expectancy effects on pain perception.

Simulating early adoption of alternative fuel vehicles for sustainability

We quantify the conditions that might trigger wide spread adoption of alternative fuel vehicles (AFVs) to support energy policy. Empirical review shows that early adopters are heterogeneous motivated by financial benefits, environmental appeal, new technology, and vehicle reliability. A probabilistic Monte Carlo simulation model is used to assess consumer heterogeneity for early and mass market adopters. For early adopters full battery electric vehicles (BEVs) are competitive but unable to surpass diesels or hybrids due to purchase price premium and lack of charging availability. For mass adoption, simulations indicate that if the purchase price premium of a BEV closes to within 20% of an in-class internal combustion engine (ICE) vehicle, combined with a 60% increase in refuelling availability relative to the incumbent system, BEVs become competitive. But this depends on a mass market that values the fuel economy and CO2 reduction benefits associated with BEVs. We also find that the largest influence on early adoption is financial benefit rather than pro-environmental behaviour suggesting that AFVs should be marketed by appealing to economic benefits combined with pro-environmental behaviour to motivate adoption. Monte Carlo simulations combined with scenarios can give insight into diffusion dynamics for other energy demand-side technologies. © 2012 Elsevier Inc.

Active learning of model evidence using Bayesian quadrature

Numerical integration is a key component of many problems in scientific computing, statistical modelling, and machine learning. Bayesian Quadrature is a modelbased method for numerical integration which, relative to standard Monte Carlo methods, offers increased sample efficiency and a more robust estimate of the uncertainty in the estimated integral. We propose a novel Bayesian Quadrature approach for numerical integration when the integrand is non-negative, such as the case of computing the marginal likelihood, predictive distribution, or normalising constant of a probabilistic model. Our approach approximately marginalises the quadrature model's hyperparameters in closed form, and introduces an active learning scheme to optimally select function evaluations, as opposed to using Monte Carlo samples. We demonstrate our method on both a number of synthetic benchmarks and a real scientific problem from astronomy.

Reliability analysis of deep excavation based on a semi-empirical approach

The design and construction of deep excavations in urban environment is often governed by serviceability limit state related to the risk of damage to adjacent buildings. In current practice, the assessment of excavation-induced building damage has focused on a deterministic approach. This paper presents a component/system reliability analysis framework to assess the probability that specified threshold design criteria for multiple serviceability limit states are exceeded. A recently developed Bayesian probabilistic framework is used to update the predictions of ground movements in the later stages of excavation based on the recorded deformation measurements. An example is presented to show how the serviceability performance for excavation problems can be assessed based on the component/system reliability analysis. © 2011 ASCE.

Estimation of soil properties and deformations in staged constructions based on mcmc method

This paper presents a Bayesian probabilistic framework to assess soil properties and model uncertainty to better predict excavation-induced deformations using field deformation data. The potential correlations between deformations at different depths are accounted for in the likelihood function needed in the Bayesian approach. The proposed approach also accounts for inclinometer measurement errors. The posterior statistics of the unknown soil properties and the model parameters are computed using the Delayed Rejection (DR) method and the Adaptive Metropolis (AM) method. As an application, the proposed framework is used to assess the unknown soil properties of multiple soil layers using deformation data at different locations and for incremental excavation stages. The developed approach can be used for the design of optimal revisions for supported excavation systems. © 2010 ASCE.

Bayesian updating of a unified soil compression model

Some amount of differential settlement occurs even in the most uniform soil deposit, but it is extremely difficult to estimate because of the natural heterogeneity of the soil. The compression response of the soil and its variability must be characterised in order to estimate the probability of the differential settlement exceeding a certain threshold value. The work presented in this paper introduces a probabilistic framework to address this issue in a rigorous manner, while preserving the format of a typical geotechnical settlement analysis. In order to avoid dealing with different approaches for each category of soil, a simplified unified compression model is used to characterise the nonlinear compression behavior of soils of varying gradation through a single constitutive law. The Bayesian updating rule is used to incorporate information from three different laboratory datasets in the computation of the statistics (estimates of the means and covariance matrix) of the compression model parameters, as well as of the uncertainty inherent in the model.

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semantic concept have similar latent values, and objects in different concepts have dissimilar latent values. We formulate the supervised infinite latent variable problem based on an intuitive principle of pulling objects together if they are of the same type, and pushing them apart if they are not. We then combine this principle with a flexible Indian Buffet Process prior on the latent variables. We show that the inferred supervised latent variables can be directly used to perform a nearest neighbour search for the purpose of retrieval. We introduce a new application of dynamically extending hash codes, and show how to effectively couple the structure of the hash codes with continuously growing structure of the neighbourhood preserving infinite latent feature space.

Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3.

A venerable history of classical work on autoassociative memory has significantly shaped our understanding of several features of the hippocampus, and most prominently of its CA3 area, in relation to memory storage and retrieval. However, existing theories of hippocampal memory processing ignore a key biological constraint affecting memory storage in neural circuits: the bounded dynamical range of synapses. Recent treatments based on the notion of metaplasticity provide a powerful model for individual bounded synapses; however, their implications for the ability of the hippocampus to retrieve memories well and the dynamics of neurons associated with that retrieval are both unknown. Here, we develop a theoretical framework for memory storage and recall with bounded synapses. We formulate the recall of a previously stored pattern from a noisy recall cue and limited-capacity (and therefore lossy) synapses as a probabilistic inference problem, and derive neural dynamics that implement approximate inference algorithms to solve this problem efficiently. In particular, for binary synapses with metaplastic states, we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that are completely constrained by the synaptic plasticity rule, and the statistics of the stored patterns and of the recall cue. Our theory organises into a coherent framework a wide range of existing data about the regulation of excitability, feedback inhibition, and network oscillations in area CA3, and makes novel and directly testable predictions that can guide future experiments.

GPU-accelerated optimization of fuel treatments for mitigating wildfire hazard

Fuel treatment is considered a suitable way to mitigate the hazard related to potential wildfires on a landscape. However, designing an optimal spatial layout of treatment units represents a difficult optimization problem. In fact, budget constraints, the probabilistic nature of fire spread and interactions among the different area units composing the whole treatment, give rise to challenging search spaces on typical landscapes. In this paper we formulate such optimization problem with the objective of minimizing the extension of land characterized by high fire hazard. Then, we propose a computational approach that leads to a spatially-optimized treatment layout exploiting Tabu Search and General-Purpose computing on Graphics Processing Units (GPGPU). Using an application example, we also show that the proposed methodology can provide high-quality design solutions in low computing time. © 2013 The Authors. Published by Elsevier B.V.

The rocking response of large flexible structures to earthquakes

The rocking response of structures subjected to strong ground motions is a problem of 'several scales'. While small structures are sensitive to acceleration pulses acting successively, large structures are more significantly affected by coherent low frequency components of ground motion. As a result, the rocking response of large structures is more stable and orderly, allowing effective isolation from the ground without imminent danger of overturning. This paper aims to characterize and predict the maximum rocking response of large and flexible structures to earthquakes using an idealized structural model. To achieve this, the maximum rocking demand caused by different earthquake records was evaluated using several ground motion intensity measures. Pulse-type records which typically have high peak ground velocity and lower frequency content caused large rocking amplitudes, whereas non-pulse type records caused random rocking motion confined to small rocking amplitudes. Coherent velocity pulses were therefore identified as the primary cause of significant rocking motion. Using a suite of pulse-type ground motions, it was observed that idealized wavelets fitted to velocity pulses can adequately describe the rocking response of large structures. Further, a parametric analysis demonstrates that pulse shape parameters affect the maximum rocking response significantly. Based on these two findings, a probabilistic analysis method is proposed for estimating the maximum rocking demand to pulse-type earthquakes. The dimensionless demand maps, produced using these methods, have predictive power in the near-field provided that pulse period and amplitude can be estimated a priori. Use of this method within a probabilistic seismic demand analysis framework is briefly discussed. © 2013 Springer Science+Business Media Dordrecht.

A practical system for modelling body shapes from single view measurements

This paper describes an interactive system for quickly modelling 3D body shapes from a single image. It provides the user with a convenient way to obtain their 3D body shapes so as to try on virtual garments online. For the ease of use, we first introduce a novel interface for users to conveniently extract anthropometric measurements from a single photo, while using readily available scene cues for automatic image rectification. Then, we propose a unified probabilistic framework using Gaussian processes, which predict the body parameters from input measurements while correcting the aspect ratio ambiguity resulting from photo rectification. Extensive experiments and user studies have supported the efficacy of our system. This system is now being exploited commercially online1. © 2011. The copyright of this document resides with its authors.