Detection of large-scale concrete columns for automated bridge inspection

There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame. This is because manually inspecting bridges is a time-consuming and costly task, and some state Departments of Transportation (DOT) cannot afford the essential costs and manpower. In this paper, a novel method that can detect large-scale bridge concrete columns is proposed for the purpose of eventually creating an automated bridge condition assessment system. The method employs image stitching techniques (feature detection and matching, image affine transformation and blending) to combine images containing different segments of one column into a single image. Following that, bridge columns are detected by locating their boundaries and classifying the material within each boundary in the stitched image. Preliminary test results of 114 concrete bridge columns stitched from 373 close-up, partial images of the columns indicate that the method can correctly detect 89.7% of these elements, and thus, the viability of the application of this research.

Large Scale Column Detection for Bridge Inspection

Manually inspecting bridges is a time-consuming and costly task. There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame as some state DOTs cannot afford the essential costs and manpower. This paper presents a novel method that can detect bridge concrete columns from visual data for the purpose of eventually creating an automated bridge condition assessment system. The method employs SIFT feature detection and matching to find overlapping areas among images. Affine transformation matrices are then calculated to combine images containing different segments of one column into a single image. Following that, the bridge columns are detected by identifying the boundaries in the stitched image and classifying the material within each boundary. Preliminary test results using real bridge images indicate that most columns in stitched images can be correctly detected and thus, the viability of the application of this research.

Uncertainty management for on-line optimisation of a POMDP-based large-scale spoken dialogue system

The optimization of dialogue policies using reinforcement learning (RL) is now an accepted part of the state of the art in spoken dialogue systems (SDS). Yet, it is still the case that the commonly used training algorithms for SDS require a large number of dialogues and hence most systems still rely on artificial data generated by a user simulator. Optimization is therefore performed off-line before releasing the system to real users. Gaussian Processes (GP) for RL have recently been applied to dialogue systems. One advantage of GP is that they compute an explicit measure of uncertainty in the value function estimates computed during learning. In this paper, a class of novel learning strategies is described which use uncertainty to control exploration on-line. Comparisons between several exploration schemes show that significant improvements to learning speed can be obtained and that rapid and safe online optimisation is possible, even on a complex task. Copyright © 2011 ISCA.

Scale-invariant vote-based 3D recognition and registration from point clouds

This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2013 Springer-Verlag Berlin Heidelberg.

Energy use of buildings at urban scale: A case study of london school buildings

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop building stock models. This research proposes an engineering-based bottom-up stock model in a probabilistic manner to address these issues. School buildings are used for illustrating the application of this probabilistic method. Two sampling-based global sensitivity methods are used to identify key factors affecting building energy performance. The sensitivity analysis methods can also create statistical regression models for inverse analysis, which are used to estimate input information for building stock energy models. The effects of different energy saving measures are analysed by changing these building stock input distributions.

Large scale heterogeneous networks, the Davis-Wielandt shell, and graph separation

We consider a large scale network of interconnected heterogeneous dynamical components. Scalable stability conditions are derived that involve the input/output properties of individual subsystems and the interconnection matrix. The analysis is based on the Davis-Wielandt shell, a higher dimensional version of the numerical range with important convexity properties. This can be used to allow heterogeneity in the agent dynamics while relaxing normality and symmetry assumptions on the interconnection matrix. The results include small gain and passivity approaches as special cases, with the three dimensional shell shown to be inherently connected with corresponding graph separation arguments. © 2012 Society for Industrial and Applied Mathematics.

Large-eddy simulations and measurements of a small-scale high-speed coflowing jet

Measurements and predictions are made of a short-cowl coflowing jet with a bypass ratio of 8:1. The Reynolds number is 300,000, and the inlet Mach numbers are representative of aeroengine conditions. The low Reynolds number of the measurements makes the case well suited to the assessment of large-eddy-simulation-related strategies. The nozzle concentricity is carefully controlled to deal with the emerging metastability issues of jets with coflow. Measurements of mean quantities and turbulence statistics are made using both laser Doppler anemometry and particle image velocimetry. The simulations are completed on 6× 106, 12× 106, and 50 × 106 cell meshes. To overcome near-wall modeling problems, a hybrid large-eddy-simulation-Reynolds-averaged-Navier-Stokesrelated method is used. The near-wall Reynolds-averaged-Navier-Stokes layer is helpful in preventing nonphysical separation from the nozzle wall.Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc.

Comparison of les to LDA and PIV measurements of a small scale high speed coflowing jet

Measurements and predictions are made of a short cowl co-flowing jet with a bypass ratio of 8:1. The Reynolds number for computations and measurements are matched at 300,000 and the Mach numbers representative of realistic jet conditions with core and co flow velocities of 240m/s and 216m/s respectively. The low Reynolds number of the measurements makes the case well suited to the assessment of large eddy resolving computational strategies. Also, the nozzle concentricity was carefully controlled to deal with the emerging metastability issues of jets with coflow. Measurements of mean quantities and turbulence statistics are made using both two dimensional coincident LDA and PIV systems. The computational simulations are completed on a modest 12×106 mesh. The simulation is now being run on a 50×106 mesh using hybrid RANSNLES (Numerical Large Eddy Simulation). Close to the nozzle wall a k-l RANS model is used. For an axisymmetric jet, comparison is made between simulations which use NLES, RANSNLES and also a simple imposed velocity profile where the nozzle is not modeled. The use of a near wall RANS model is shown to be beneficial. When compared with the measurements the NLES results are encouraging. Copyright © 2008 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.