Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination

Measurement of operator quality performance has been challenging in the construction fabrication industry. Among various causes, the learning effect is a significant factor, which needs to be incorporated in achieving a reliable operator quality performance analysis. This research aims to enhance a previously developed operator quality performance measurement approach by incorporating the learning effect (i.e., work experience). To achieve this goal, the Plateau learning model is selected to quantitatively represent the relationship between quality performance and work experience through a beta-binomial regression approach. Based on this relationship, an informative prior determination approach, which incorporates operator work experience information, is developed to enhance the previous Bayesian-based operator quality performance measurement. Academically, this research provides a systematic approach to derive Bayesian informative priors through integrating multi-source information. Practically, the proposed approach reliably measures operator quality performance in fabrication quality control processes.Comment: 8 pages, 5 figures, 2 tables, i3CE 201

Nondestructive Evaluation with Beamforming Transducer Arrays

If a nondestructive evaluation system is designed to detect the presence or absence of a flaw in a material, typically one transducer may be sufficient. If, however, a characterization of the flaw is desired, then an array of transducers is in most cases required. Besides the capability of two and three dimensional imaging, array data has the advantages of increased resolution, improved signal-to-noise ratio after preprocessing and sharper focusing. In any NDE system, the acquisition of data is only one step towards the final objective of flaw characterization. The other step is that of processing the data in order to extract the desired information. In this paper, we consider one signal processing aspect of data obtained by a linear array of transducers. Each element on the array normally operates as transmitter and receiver simultaneously, and the data is collected by exciting one transducer at a time. The measured signals, after suitable time shifting for alignment, are summed in order to focus (or beamsteer) the array at a specific point. The resolution of this summing process depends on the side lobes of the array reject response, and this in turn depends on the number of elements and spacing between elements on the array. While summing is the simplest signal processing procedure to perform, it is however, as far as beamforming is concerned, not the most effective. The side lobe levels decrease as the number of elements N increases, and this has a lower bound of about -14 dB as N →∞. In this paper, we introduce an additional processing step with specially designed optimum filters before summing. The design methodology for these filters will be discussed in detail, and it will be shown that these filters have a superior frequency reject response which becomes more apparent if the array has a small number of elements

Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination

Measurement of operator quality performance has been challenging in the construction fabrication industry. Among various causes, the learning effect is a significant factor, which needs to be incorporated in achieving a reliable operator quality performance analysis. This research aims to enhance a previously developed operator quality performance measurement approach by incorporating the learning effect (i.e., work experience). To achieve this goal, the Plateau learning model is selected to quantitatively represent the relationship between quality performance and work experience through a beta-binomial regression approach. Based on this relationship, an informative prior determination approach, which incorporates operator work experience information, is developed to enhance the previous Bayesian-based operator quality performance measurement. Academically, this research provides a systematic approach to derive Bayesian informative priors through integrating multi-source information. Practically, the proposed approach reliably measures operator quality performance in fabrication quality control processes.Comment: 8 pages, 5 figures, 2 tables, i3CE 201

Jacobian-Based Iterative Method for Magnetic Localization in Robotic Capsule Endoscopy

The purpose of this study is to validate a Jacobian-based iterative method for real-time localization of magnetically controlled endoscopic capsules. The proposed approach applies finite-element solutions to the magnetic field problem and least-squares interpolations to obtain closed-form and fast estimates of the magnetic field. By defining a closed-form expression for the Jacobian of the magnetic field relative to changes in the capsule pose, we are able to obtain an iterative localization at a faster computational time when compared with prior works, without suffering from the inaccuracies stemming from dipole assumptions. This new algorithm can be used in conjunction with an absolute localization technique that provides initialization values at a slower refresh rate. The proposed approach was assessed via simulation and experimental trials, adopting a wireless capsule equipped with a permanent magnet, six magnetic field sensors, and an inertial measurement unit. The overall refresh rate, including sensor data acquisition and wireless communication was 7 ms, thus enabling closed-loop control strategies for magnetic manipulation running faster than 100 Hz. The average localization error, expressed in cylindrical coordinates was below 7 mm in both the radial and axial components and 5° in the azimuthal component. The average error for the capsule orientation angles, obtained by fusing gyroscope and inclinometer measurements, was below 5°

Domain-specific risk assessment using integrated simulation: A case study of an onshore wind project

Although many quantitative risk assessment models have been proposed in literature, their use in construction practice remain limited due to a lack of domain-specific models, tools, and application examples. This is especially true in wind farm construction, where the state-of-the-art integrated Monte Carlo simulation and critical path method (MCS-CPM) risk assessment approach has yet to be demonstrated. The present case study is the first reported application of the MCS-CPM method for risk assessment in wind farm construction and is the first case study to consider correlations between cost and schedule impacts of risk factors using copulas. MCS-CPM provided reasonable risk assessment results for a wind farm project, and its use in practice is recommended. Aimed at facilitating the practical application of quantitative risk assessment methods, this case study provides a much-needed analytical generalization of MCS-CPM, offering application examples, discussion of expected results, and recommendations to wind farm construction practitioners

Copper-catalyzed diastereo- and enantioselective desymmetrization of cyclopropenes: Synthesis of cyclopropylboronates

This document is the accepted manuscript version of a Published Work that appeared in final form in Journal of American Chemical Society 136.45, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see DOI: 10.1021/ja510419zA novel Cu-catalyzed diastereo- and enantioselective desymmetrization of cyclopropenes to afford nonracemic cyclopropylboronates is described. Trapping the cyclopropylcopper intermediate with electrophilic amines allows for the synthesis of cyclopropylaminoboronic esters and demonstrates the potential of the approach for the synthesis of functionalized cyclopropanesWe thank the European Research Council (ERC-337776) and MINECO (CTQ2012-35957) for financial support. M. T. and A. P. thank MICINN for RyC and JdC contract