Expert system for prestressed concrete bridge design

The development of an expert system, BRIDEX, for the design of prestressed concrete bridges is discussed in this paper. Design of multi-span continuous pre-stressed concrete bridges pose considerable difficulties to designers because of the large number of parameters involved and their complex interactions. The design is often perceived as an iterative process of generation, evaluation and modification of trial designs. It takes years of experience to develop an understanding of the design process. BRIDEX is aimed at providing guidance to the designers by suggesting appropriate range of values for the design parameters. The knowledge within BRIDEX is mainly based on fundamental principles developed by a careful study of the intricacies involved in the design process, while heuristics are used only to supplement this knowledge. The BRIDEX approach ensures that the whole design evolves sequentially as the design proceeds, module after module.

Foot contact detection for sprint training

We introduce a new algorithm to automatically identify the time and pixel location of foot contact events in high speed video of sprinters. We use this information to autonomously synchronise and overlay multiple recorded performances to provide feedback to athletes and coaches during their training sessions. The algorithm exploits the variation in speed of different parts of the body during sprinting. We use an array of foreground accumulators to identify short-term static pixels and a temporal analysis of the associated static regions to identify foot contacts. We evaluated the technique using 13 videos of three sprinters. It successfully identifed 55 of the 56 contacts, with a mean localisation error of 1.39±1.05 pixels. Some videos were also seen to produce additional, spurious contacts. We present heuristics to help identify the true contacts. © 2011 Springer-Verlag Berlin Heidelberg.

Construction of nonparametric Bayesian models from parametric Bayes equations

We consider the general problem of constructing nonparametric Bayesian models on infinite-dimensional random objects, such as functions, infinite graphs or infinite permutations. The problem has generated much interest in machine learning, where it is treated heuristically, but has not been studied in full generality in non-parametric Bayesian statistics, which tends to focus on models over probability distributions. Our approach applies a standard tool of stochastic process theory, the construction of stochastic processes from their finite-dimensional marginal distributions. The main contribution of the paper is a generalization of the classic Kolmogorov extension theorem to conditional probabilities. This extension allows a rigorous construction of nonparametric Bayesian models from systems of finite-dimensional, parametric Bayes equations. Using this approach, we show (i) how existence of a conjugate posterior for the nonparametric model can be guaranteed by choosing conjugate finite-dimensional models in the construction, (ii) how the mapping to the posterior parameters of the nonparametric model can be explicitly determined, and (iii) that the construction of conjugate models in essence requires the finite-dimensional models to be in the exponential family. As an application of our constructive framework, we derive a model on infinite permutations, the nonparametric Bayesian analogue of a model recently proposed for the analysis of rank data.

Distribution matching for transduction

Many transductive inference algorithms assume that distributions over training and test estimates should be related, e.g. by providing a large margin of separation on both sets. We use this idea to design a transduction algorithm which can be used without modification for classification, regression, and structured estimation. At its heart we exploit the fact that for a good learner the distributions over the outputs on training and test sets should match. This is a classical two-sample problem which can be solved efficiently in its most general form by using distance measures in Hilbert Space. It turns out that a number of existing heuristics can be viewed as special cases of our approach.

Cubically convergent iterations for invariant subspace computation

We propose a Newton-like iteration that evolves on the set of fixed dimensional subspaces of ℝ n and converges locally cubically to the invariant subspaces of a symmetric matrix. This iteration is compared in terms of numerical cost and global behavior with three other methods that display the same property of cubic convergence. Moreover, we consider heuristics that greatly improve the global behavior of the iterations.

Systematic misregistration and the statistical analysis of surface data.

Spatial normalisation is a key element of statistical parametric mapping and related techniques for analysing cohort statistics on voxel arrays and surfaces. The normalisation process involves aligning each individual specimen to a template using some sort of registration algorithm. Any misregistration will result in data being mapped onto the template at the wrong location. At best, this will introduce spatial imprecision into the subsequent statistical analysis. At worst, when the misregistration varies systematically with a covariate of interest, it may lead to false statistical inference. Since misregistration generally depends on the specimen's shape, we investigate here the effect of allowing for shape as a confound in the statistical analysis, with shape represented by the dominant modes of variation observed in the cohort. In a series of experiments on synthetic surface data, we demonstrate how allowing for shape can reveal true effects that were previously masked by systematic misregistration, and also guard against misinterpreting systematic misregistration as a true effect. We introduce some heuristics for disentangling misregistration effects from true effects, and demonstrate the approach's practical utility in a case study of the cortical bone distribution in 268 human femurs.

Stable dynamic walking over uneven terrain

We propose a constructive control design for stabilization of non-periodic trajectories of underactuated robots. An important example of such a system is an underactuated "dynamic walking" biped robot traversing rough or uneven terrain. The stabilization problem is inherently challenging due to the nonlinearity, open-loop instability, hybrid (impact) dynamics, and target motions which are not known in advance. The proposed technique is to compute a transverse linearization about the desired motion: a linear impulsive system which locally represents "transversal" dynamics about a target trajectory. This system is then exponentially stabilized using a modified receding-horizon control design, providing exponential orbital stability of the target trajectory of the original nonlinear system. The proposed method is experimentally verified using a compass-gait walker: a two-degree-of-freedom biped with hip actuation but pointed stilt-like feet. The technique is, however, very general and can be applied to a wide variety of hybrid nonlinear systems. © The Author(s) 2011.

Stable dynamic walking over rough terrain: Theory and experiment

We propose a constructive control design for stabilization of non-periodic trajectories of underactuated mechanical systems. An important example of such a system is an underactuated "dynamic walking" biped robot walking over rough terrain. The proposed technique is to compute a transverse linearization about the desired motion: a linear impulsive system which locally represents dynamics about a target trajectory. This system is then exponentially stabilized using a modified receding-horizon control design. The proposed method is experimentally verified using a compass-gait walker: a two-degree-of-freedom biped with hip actuation but pointed stilt-like feet. The technique is, however, very general and can be applied to higher degree-of-freedom robots over arbitrary terrain and other impulsive mechanical systems. © 2011 Springer-Verlag.

Theoretical study of line narrowing in a sub-terahertz monolithic dual laser source with an integrated reflector

A monolithic design is proposed for low-noise sub-THz signal generation by integrating a reflector onto a dual laser source. The reflectivity and the position of such a reflector can be adjusted to obtain constructive feedback from the reflector to both lasers, thus causing a Vernier feedback effect. As a result, 10-fold line narrowing, the narrowing being limited by the resolution of the simulation, is predicted using a transmission line model. Finally, a simple control scheme using an electrical feedback loop to adjust laser biases is proposed to maintain the line narrowing performance. This line narrowing technique, comprising a passive integrated reflector, could allow the development of a low-cost, compact and energy-efficient solution for high-purity sub-THz signal generation. © The Institution of Engineering and Technology 2014.

Guidewire retention following central venous catheterisation: a human factors and safe design investigation.

BACKGROUND: Central Venous Catheterisation (CVC) has occasionally been associated with cases of retained guidewires in patients after surgery. In theory, this is a completely avoidable complication; however, as with any human procedure, operator error leading to guidewires being occasionally retained cannot be fully eliminated. OBJECTIVE: The work described here investigated the issue in an attempt to better understand it both from an operator and a systems perspective, and to ultimately recommend appropriate safe design solutions that reduce guidewire retention errors. METHODS: Nine distinct methods were used: observations of the procedure, a literature review, interviewing CVC end-users, task analysis construction, CVC procedural audits, two human reliability assessments, usability heuristics and a comprehensive solution survey with CVC end-users. RESULTS: The three solutions that operators rated most highly, in terms of both practicality and effectiveness, were: making trainees better aware of the potential guidewire complications and strongly emphasising guidewire removal in CVC training, actively checking that the guidewire is present in the waste tray for disposal, and standardising purchase of central line sets so that differences that may affect chances of guidewire loss is minimised. CONCLUSIONS: Further work to eliminate/engineer out the possibility of guidewires being retained is proposed.

New robotics: design principles for intelligent systems.

New robotics is an approach to robotics that, in contrast to traditional robotics, employs ideas and principles from biology. While in the traditional approach there are generally accepted methods (e. g., from control theory), designing agents in the new robotics approach is still largely considered an art. In recent years, we have been developing a set of heuristics, or design principles, that on the one hand capture theoretical insights about intelligent (adaptive) behavior, and on the other provide guidance in actually designing and building systems. In this article we provide an overview of all the principles but focus on the principles of ecological balance, which concerns the relation between environment, morphology, materials, and control, and sensory-motor coordination, which concerns self-generated sensory stimulation as the agent interacts with the environment and which is a key to the development of high-level intelligence. As we argue, artificial evolution together with morphogenesis is not only "nice to have" but is in fact a necessary tool for designing embodied agents.