Celestino Ordóñez

A Conceptual Model for Analyzing the Risks Involved in the Transportation of Hazardous Goods: Implementation in a Geographic Information System

The transportation of hazardous goods by road implies a risk for both humans and the environment, in that an accident involving a vehicle transporting this kind of material may cause extensive material and environmental damage and might even endanger lives. For this reason, both public and private entities (e.g., insurance companies) have a growing interest in studies that assess the risks associated with hazardous goods transportation This article describes a method for calculating these risks. The risk is determined on the basis of a calculation of the probability of the occurrence of an accident and the gravity of the damage, which is in turn a function of the potential damage inherent in the goods being transported taken with the vulnerability of the environmental medium in which the accident takes place. The mathematical model proposed is easily implemented in a geographical information system that will produce risk maps delimiting the more potentially conflictive stretches of roadway.

Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX) anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees.

Using model-based geostatistics to predict lightning-caused wildfires

The probability of fire in a particular area depends on a range of environmental and geographic variables. Fire prevention planning can be assisted by the construction of models to identify the variables that have a significant influence on the occurrence of fires and by building maps showing the spatial probability distribution for fires occurring in specific geographic areas. We used generalized spatial linear models to predict spatially distributed probabilities for fire occurrence in locations where storms featuring lightning occurred, on the basis of a set of variables related to climatology, orography, vegetation and lightning characteristics, and to assess the relative importance of these variables. A comparison of this model with simple logistic regression models used by other researchers to resolve similar problems demonstrates the importance of bearing in mind spatial correlation between variables.

Application of close range photogrammetry to deck measurement in recreational ships.

In this article, we present results that demonstrate the utility of close range photogrammetry in the measurement of decks in recreational craft as an alternate measurement system to the one based on direct acquisition of coordinates. The areas of deck covered with teakwood for aesthetic or security reasons were measured. Both methods were compared in terms of precision of measurements, time consumption, equipment cost, and ease of manipulation and equipment transportation. Based on the results, we conclude that photogrammetry has advantages in almost every aspect with respect to the direct method. Consequently, photogrammetry is suggested as a suitable method for coordinate measurement of decks in recreational ships. However, in some special circumstances, where ships have wide corridors with few obstacles the direct method can be more appropriate than the photogrammetric method.

Machine learning techniques applied to the determination of osteoporosis incidence in post-menopausal women

Osteoporosis is a disease that mostly affects women in developed countries. It is characterised by reduced bone mineral density (BMD) and results in a higher incidence of fractured or broken bones. In this research we studied the relationship between BMD and diet and lifestyle habits for a sample of 305 post-menopausal women by constructing a non-linear model using the regression support vector machines technique. One aim of this model was to make an initial preliminary estimate of BMD in the studied women (on the basis of a questionnaire with questions mostly on dietary habits) so as to determine whether they needed densitometry testing. A second aim was to determine the factors with the greatest bearing on BMD with a view to proposing dietary and lifestyle improvements. These factors were determined using regression trees applied to the support vector machines predictions.

Functional support vector machines and generalized linear models for glacier geomorphology analysis

We propose a functional pattern recognition approach to the problem of identifying the topographic profiles of glacial and fluvial valleys, using a functional version of support vector machines (SVMs) for classification. We compare a proposed functional version of SVMs with functional generalized linear models and their vectorial versions: generalized linear models and SVMs that use the original observations as input. The results indicate the benefit of our proposed functional SVMs and, in more general terms, the advantages of using a functional rather than a vectorial approach.

Forecasting SO2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models

An SO2 emission episode at coal-fired power station occurs when the series of bihourly average of SO2 concentration, taken at 5-minute intervals, is greater than a specific value. Advance prediction of these episodes of pollution is very important for companies generating electricity by burning coal since it allows them to take appropriate preventive measures. In order to forecast SO2 pollution episodes, three different methods were tested: Elman neural networks, autoregressive integrated moving average (ARIMA) models, and a hybrid method combining both. The three methods were applied to a time series of SO2 concentrations registered in a control station in the vicinity of a coal-fired power station. The results obtained showed a better performance of the hybrid method over the Elman networks and the ARIMA models. The best prediction was obtained 115 minutes in advance by the hybrid model.

3D TERRESTRIAL LASER TECHNOLOGY IN SPORTING CRAFT 3D MODELLING

The urgent need to increase productivity and competitiveness demands from sporting craft builders the incorporation of newer design and manufacturing technologies, as CAD/CAM and CNC machining systems. This paper describes the sporting boats’ 3D surface modelling, needed for automated manufacturing processes, throug terrestrial laser scanner technology. The methodology followed for data collection and data processiong is described in detail, advantages and limitations of this technique are displayed and the accuraccy of the obtained results is also estimated. According to obtained results, this technology proves to provide accurate results and to be cost and time effective. (Extended paper from the International Symposium CompIMAGE – Coimbra, Portugal, 20-21 October 2006.)

Geostatistical study of the feldspar content and quality of a granite deposit

Abstract In order to characterise the saleable feldspar in a granite deposit, a methodology was developed in accordance with the exploitation process. This consisted of mechanically extracting the surface layer of the batholith and separating the feldspar from the quartz using the granulometric separation method, given that the size of the grains of the feldspar is greater than that of quartz. Following washing, grinding and magnetic separation of the feldspar in order to eliminate the ferromagnesium minerals, the saleable feldspar was characterised in terms of the factors that determine its market value, namely, its content in Al 2 O 3 , SiO 2 , Na 2 O and K 2 O. Following the opening of prospecting pits in the granite massif, samples were analysed in the laboratory using three different granulometric cuts and by reproducing the treatment process. The values for the quality variables of saleable feldspar were obtained, and the results were interpolated to the entire deposit using the kriging method. In order to summarise the information from the above-mentioned variables, a quality index was constructed using multivariate statistics and by employing market criteria, and subsequently, the values of the index were interpolated to the entire deposit using bidimensional kriging. The map of saleable quality feldspar from the deposit permits both affirmation of the treatment process yield for each granulometric cut and the planning of extraction from the deposit to obtain a homogeneous quality in the saleable feldspar.

A methodology for the inventory of historical infrastructures: documentation, current state, and influencing factors

This study is focused on the development of a methodology for the study of historical bridges. The discussion is divided into several parts: 1) a description of a simple methodology to quickly acquire data during the inspection of historical bridges; 2) a report on the current state of 88 historical bridges located in the Galician Country; 3) a data storage system for automated diagnosis of bridges, based on criteria determined by an expert staff; and 4) an exploration of trends and other factors can be related to the conservation of these bridges. Some of the more important bridge features that were obtained in this project are the geometry, construction, pathologies and structural damage in the bridge. The data were managed using digital databases and GIS. This approach enabled focused, deep inspection for bridges with urgent problems. Finally, results are presented with the conclusion that public policies to maintain bridges have not been sufficient. In fact, conservation actions have normally been carried out only after problems are detected, so public authorities apply corrective measures instead of preventive ones.