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Boecillo, Spain

Gomez-Garcia-Bermejo J.,University of Valladolid | Zalama E.,University of Valladolid | Feliz R.,CARTIF Foundation
Computer-Aided Civil and Infrastructure Engineering

Laser range sensors are playing an increased role in construction. These devices are used to collect a large number of points from different locations and then, those points are registered in a common framework. This article describes a new procedure for the registration of point clouds, especially suited to the fields of architecture and cultural heritage. Often, in these fields, the registration of point clouds is subject to errors due to the fact that an important number of points do not lie on particular geometric features. In this article, an accurate and efficient approach for 3D data registration based on Iterative Closest Point (ICP) algorithm is proposed, which takes advantage of the color data acquired along with range data. Points suitable for registration are selected according to their local geometry and/or color properties, thus a significant improvement on performance convergence and processing time is obtained. The algorithm performs an automatic, on-the-flight estimation of the overlapping region, taking into account possible color differences produced by lighting changes through the measurement process. The proposed approach has been tested on real scanned data from cultural heritage buildings and compared to other approaches, showing a better performance in terms of automation degree, accuracy, and speed. © 2012 Computer-Aided Civil and Infrastructure Engineering. Source

Bustillo A.,University of Burgos | Correa M.,Technical University of Madrid | Renones A.,CARTIF Foundation

The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling xprocess is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases. © 2011 by the authors; licensee MDPI, Basel, Switzerland. Source

Kontes G.D.,Technical University of Crete | Valmaseda C.,CARTIF Foundation | Giannakis G.I.,Technical University of Crete | Katsigarakis K.I.,Technical University of Crete | And 2 more authors.
Journal of Process Control

The topic of optimized building operation has attracted considerable interest in the research community: in this context model-based supervisory control design approaches have been shown to yield effective/optimized operation with regards to energy performance or other related operational parameters. A hindrance towards the adoption of such methodologies is the need for a mathematical model tailored to each building which is capable of capturing all pertinent dynamics. Developing and tuning such a model can be a time-consuming and costly proposition, and is the main reason why such approaches have found little applicability beyond the research space. The utilization of models constructed in the building design phases - for the reason of estimating energy performance - properly adapted for the task at hand can be a viable methodology to overcome this problem. We present in this paper, an online process where a stochastic optimization algorithm utilizing a detailed thermal simulation model of the building along with historical sensor measurements and weather and occupancy forecasts, is used to design effective control strategies for a predefined period. A detailed description of the methodology is provided and the proposed approach is evaluated on a heating experiment conducted in a real building located in Greece. © 2014 Elsevier Ltd. All rights reserved. Source

Villa L.F.,CARTIF Foundation | Renones A.,CARTIF Foundation | Peran J.R.,CARTIF Foundation | De Miguel L.J.,University of Valladolid
Mechanical Systems and Signal Processing

This work presents the development of an angular resampling algorithm for applying in conditions of high speed variability, as occurs in wind turbines, and the results obtained when applied to simulated signals, bearings diagnostic test-beds and wind turbines. The results improve the accuracy of similar resampling algorithms offered by the consulted bibliography. This algorithm is part of the wind turbine diagnostic system developed by the authors. © 2010 Elsevier Ltd. All rights reserved. Source

Medina R.,CARTIF Foundation | Gomez-Garcia-Bermejo J.,University of Valladolid | Zalama E.,CARTIF Foundation
2010 - 27th International Symposium on Automation and Robotics in Construction, ISARC 2010

Pavement maintenance requires knowing the state of the road surface. Human inspection is the most common method for evaluating this state. Recently, the automated visual inspection has been addressed, but some important questions remain open concerning the variable ambient lighting, shadows, device synchronisation and the large amount of data. In the present paper, an automated visual inspection system is presented. Images are obtained using laser lighting and linear cameras onboard a vehicle. Longitudinal and transversal cracks are detected and classified using a novel approach based on combining traditional features and Gabor filters. A Differential Global Positioning System (DGPS), a web camera and an Inertial Profiler to measure the International Roughness Index (IRI) are also considered in order to obtain comprehensive information about the road state. Implementation details are given concerning image acquisition and processing, system architecture and data synchronisation. Field results are presented which prove the suitability of the approach. Source

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