Wave propagation in nonlinear one-dimensional soil model

The objective of the research conducted by the authors is to explore the feasibility of determining reliable in situ values of shear modulus as a function of strain. In this paper the meaning of the material stiffness obtained from impact and harmonic excitation tests on a surface slab is discussed. A one-dimensional discrete model with the nonlinear material stiffness is used for this purpose. When a static load is applied followed by an impact excitation, if the amplitude of the impact is very small, the measured wave velocity using the cross-correlation indicates the wave velocity calculated from the tangent modulus corresponding to the state of stress caused by the applied static load. The duration of the impact affects the magnitude of the displacement and the particle velocity but has very little effect on the estimation of the wave velocity for the magnitudes considered herein. When a harmonic excitation is applied, the cross-correlation of the time histories at different depths estimates a wave velocity close to the one calculated from the secant modulus in the stress-strain loop under steady-state condition. Copyright © 2008 John Wiley & Sons, Ltd.

Unified compression model for venice lagoon natural silts

Over the last 50 years, the city of Venice, Italy, has observed a significant increase in the frequency of flooding. Numerous engineering solutions have been proposed, including the use of movable gates located at the three lagoon inlets. A key element in the prediction of performance is the estimation of settlements of the foundation system of the gates. The soils of Venice Lagoon are characterized by very erratic depositional patterns of clayey silts, resulting in an extremely heterogeneous stratigraphy with discontinuous layering. The soils are also characterized by varying contents of coarse and fine-grained particles. In contrast, the mineralogical composition of these deposits is quite uniform, which allows us to separate the influence of mineralogy from that of grain size distribution. A comprehensive geotechnical testing program was performed to assess the one-dimensional compression of Venice soils and examine the factors affecting the response in the transition from one material type to another. The compressibility of these natural silty clayey soils can be described by a single set of constitutive laws incorporating the relative fraction of granular to cohesive material. © 2007 ASCE.

Using sub-word-level information for confidence estimation with conditional random field models

The task of word-level confidence estimation (CE) for automatic speech recognition (ASR) systems stands to benefit from the combination of suitably defined input features from multiple information sources. However, the information sources of interest may not necessarily operate at the same level of granularity as the underlying ASR system. The research described here builds on previous work on confidence estimation for ASR systems using features extracted from word-level recognition lattices, by incorporating information at the sub-word level. Furthermore, the use of Conditional Random Fields (CRFs) with hidden states is investigated as a technique to combine information for word-level CE. Performance improvements are shown using the sub-word-level information in linear-chain CRFs with appropriately engineered feature functions, as well as when applying the hidden-state CRF model at the word level.

Cortical thickness estimation of the proximal femur from multi-view Dual-Energy X-ray Absorptiometry (DXA)

Hip fracture is the leading cause of acute orthopaedic hospital admission amongst the elderly, with around a third of patients not surviving one year post-fracture. Although various preventative therapies are available, patient selection is difficult. The current state-of-the-art risk assessment tool (FRAX) ignores focal structural defects, such as cortical bone thinning, a critical component in characterizing hip fragility. Cortical thickness can be measured using CT, but this is expensive and involves a significant radiation dose. Instead, Dual-Energy X-ray Absorptiometry (DXA) is currently the preferred imaging modality for assessing hip fracture risk and is used routinely in clinical practice. Our ambition is to develop a tool to measure cortical thickness using multi-view DXA instead of CT. In this initial study, we work with digitally reconstructed radiographs (DRRs) derived from CT data as a surrogate for DXA scans: this enables us to compare directly the thickness estimates with the gold standard CT results. Our approach involves a model-based femoral shape reconstruction followed by a data-driven algorithm to extract numerous cortical thickness point estimates. In a series of experiments on the shaft and trochanteric regions of 48 proximal femurs, we validated our algorithm and established its performance limits using 20 views in the range 0°-171°: estimation errors were 0:19 ± 0:53mm (mean +/- one standard deviation). In a more clinically viable protocol using four views in the range 0°-51°, where no other bony structures obstruct the projection of the femur, measurement errors were -0:07 ± 0:79 mm. © 2013 SPIE.

Bayesian Learning of Noisy Markov Decision Processes

We consider the inverse reinforcement learning problem, that is, the problem of learning from, and then predicting or mimicking a controller based on state/action data. We propose a statistical model for such data, derived from the structure of a Markov decision process. Adopting a Bayesian approach to inference, we show how latent variables of the model can be estimated, and how predictions about actions can be made, in a unified framework. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from the posterior distribution. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.

Unconstrained monocular 3D human pose estimation by action detection and cross-modality regression forest

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts. © 2013 IEEE.

Calibrating the Gaussian multi-target tracking model

We present novel batch and online (sequential) versions of the expectation-maximisation (EM) algorithm for inferring the static parameters of a multiple target tracking (MTT) model. Online EM is of particular interest as it is a more practical method for long data sets since in batch EM, or a full Bayesian approach, a complete browse of the data is required between successive parameter updates. Online EM is also suited to MTT applications that demand real-time processing of the data. Performance is assessed in numerical examples using simulated data for various scenarios. For batch estimation our method significantly outperforms an existing gradient based maximum likelihood technique, which we show to be significantly biased. © 2014 Springer Science+Business Media New York.

Estimation of the natural purification rate of a eutrophic lake after pollutant removal

Pollution resulting from increased human activities is threatening Lake Donghu, its effects being characterized by serious eutrophication. A steady increase of phosphorus loading is the most important factor of the lake eutrophication. Pollution external control projects are being implemented and will be accomplished before the year 2010. In order to predict the restoration rate by the lake's self-purification after the projects of external control, a model of predicting the removal rate of total phosphorus (TP) from lake water is developed, and a brief method of estimating the release and export rate of sediment phosphorus is suggested. Results show that, on the premise of external loading fully controlled. The restoration needs about 55 years or more. Obviously, the great P pool in the sediment will be a limiting factor of preventing the improvement of water quality after the external loading is under control. Based on the estimation we conclude that after the external control projects before 2010, in order to restore the lake in a few years, although highly cost, the first step must be the sediment dredging to remove internal loading. The second step is diverting water of River Changjiang into the lake to accelerate the improvement of lake water. Otherwise, removal of pollutant sources will become meaningless. (c) 2006 Elsevier B.V. All rights reserved.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.

Potential frequency bandwidth estimation of TO packaging techniques for photodiode modules

Scattering parameters of photodiode chip, TO header and TO packaged module are measured, and the effects of TO packaging network on the high-frequency response of photodiode are investigated. Based on the analysis, the potential bandwidth of TO packaging techniques is estimated from the scattering parameters of the TO packaging network. Another method for estimating the potential bandwidth from the equivalent circuit for the TO packaged photodiode model is also presented. The results obtained using both methods show that the TO packaging techniques used in the experiments can potentially achieve a frequency bandwidth of 22 GHz.

Modern spectral estimation of ICP-AES

The inductively coupled plasma atomic emission, spectrometry (ICP-AES) and its signal characteristics were discussed using modem spectral estimation technique. The power spectra density (PSD) was calculated using the auto-regression (AR) model of modem spectra estimation. The Levinson-Durbin recursion method was used to estimate the model parameters which were used for the PSD computation. The results obtained with actual ICP-AES spectra and measurements showed that the spectral estimation technique was helpful for the better understanding about spectral composition and signal characteristics.

Cure processing modeling and cure cycle simulation of epoxy-terminated poly(phenylene ether ketone). V: Estimation of temperature distribution during cure process

A numerical method to estimate temperature distribution during the cure of epoxy-terminated poly(phenylene ether ketone) (E-PEK)-based composite is suggested. The effect of the temperature distribution on the selection of cure cycle is evaluated using a suggested alternation criterion. The effect of varying heating rate and thickness on the temperature distribution, viscosity distribution and distribution of the extent of cure reaction are discussed based on the combination of the here-established temperature distribution model and the previously established curing kinetics model and chemorheological model. It is found that, for a thin composite (<=10mm) and low heating rate (<=2.5K/min), the effect of temperature distribution on cure cycle and on the processing window for pressure application can be neglected. Low heating rate is of benefit to reduce the temperature gradient. The processing window for pressure application becomes narrower with increasing thicknesses of composite sheets. The validity of the temperature distribution model and the modified processing window is evaluated through the characterization of mechanical and physical properties of E-PEK-based composite fabricated according to different temperature distribution conditions.

Factors affecting the estimation of gas hydrate and free gas saturation

Attenuations of different types of gas hydrate cementation in fluid-saturated porous solids are discussed. The factors affecting estimation of gas hydrate and free gas saturation are analyzed. It is suggested that porosity of sediment, the P wave velocity model and methods of calculating elastic modulus are key factors in the estimation of gas hydrate and free gas saturations. Attenuation of gas hydrate-bearing sediment is closely related with the cementation types of gas hydrate. Negative anomalies of quality factors indicate that gas hydrate deposits away from grain as part of fluid. Positive anomalies of the quality factors indicate that gas hydrate contacts with solid and changes the elastic modulus of matrix. Low frequency velocity and high frequency velocity models are used to estimate gas hydrate and free gas saturation in the Blake Ridge area according to the well log data of the hole 995 in ODP leg 164. The gas hydrate saturation obtained by low frequency velocity is 10% similar to 20% of the pore space and free gas saturation is 0.5% similar to 1% of the pore space. The gas hydrate saturation obtained by high frequency velocity is 5% similar to 10% of the pore space and free gas saturation is 1% similar to 2% of the pore space.

Analysis of the sea-level variability along the Chinese coast and estimation of the impact of a CO2-perturbed atmospheric circulation

The relationship between monthly sea-level data measured at stations located along the Chinese coast and concurrent large-scale atmospheric forcing in the period 1960-1990 is examined. It is found that sea-level varies quite coherently along the whole coast, despite the geographical extension of the station set. A canonical correlation analysis between sea-level and sea-level pressure (SLP) indicates that a great part of the sea-level variability can be explained by the action of the wind stress on the ocean surface. The relationship between sea-level and sea-level pressure is analyzed separately for the summer and winter half-years. In winter, one factor affecting sea-level variability at all stations is the SLP contrast between the continent and the Pacific Ocean, hence the intensity of the winter Monsoon circulation. Another factor that affects coherently all stations is the intensity of the zonal circulation at mid-latitudes. In the summer half year, on the other hand, the influence of SLP on sea-level is spatially less coherent: the stations in the Yellow Sea are affected by a more localized circulation anomaly pattern, whereas the rest of the stations is more directly connected to the intensity of the zonal circulation. Based on this analysis, statistical models (different for summer and winter) to hindcast coastal sealevel anomalies from the large-scale SLP field are formulated. These models have been tested by fitting their internal parameters in a test period and reproducing reasonably the sea-level evolution in an independent period. These statistical models are also used to estimate the contribution of the changes of the atmospheric circulation on sea-level along the Chinese coast in an altered climate. For this purpose the ouput of 150 year-long experiment with the coupled ocean-atmosphere model ECHAM1-LSG has been analyzed, in which the atmospheric concentration of greenhouse gases was continuously increased from 1940 until 2090, according to the Scenario A projection of the Intergovermental Panel on Climate Change. In this experiment the meridional (zonal) circulation relevant for sea-level tends to become weaker (stronger) in the winter half year and stronger (weaker) in summer. The estimated contribution of this atmospheric circulation changes to coastal sea-level is of the order of a few centimeters at the end of the integration, being in winter negative in the Yellow Sea and positive in the China Sea with opposite signs in the summer half-year.