Forensic analysis of buildings affected by mining subsidence based on Differential Interferometry (Part III)

This work presents a forensic analysis of buildings affected by mining subsidence, which is based on deformation data obtained by Differential Interferometry (DInSAR). The proposed test site is La Union village (Murcia, SE Spain) where subsidence was triggered in an industrial area due to the collapse of abandoned underground mining labours occurred in 1998. In the first part of this work the study area was introduced, describing the spatial and temporal evolution of ground subsidence, through the elaboration of a cracks map on the buildings located within the affected area. In the second part, the evolution of the most significant cracks found in the most damaged buildings was monitored using biaxial extensometric units and inclinometers. This article describes the work performed in the third part, where DInSAR processing of satellite radar data, available between 1998 and 2008, has permitted to determine the spatial and temporal evolution of the deformation of all the buildings of the study area in a period when no continuous in situ instrumental data is available. Additionally, the comparison of these results with the forensic data gathered in the 2005–2008 period, reveal that there is a coincidence between damaged buildings, buildings where extensometers register significant movements of cracks, and buildings deformation estimated from radar data. As a result, it has been demonstrated that the integration of DInSAR data into forensic analysis methodologies contributes to improve significantly the assessment of the damages of buildings affected by mining subsidence.

Grouping genetic operators for the delineation of functional areas based on spatial interaction

The delineation of functional economic areas, or market areas, is a problem of high practical relevance, since the delineation of functional sets such as economic areas in the US, Travel-to-Work Areas in the United Kingdom, and their counterparts in other OECD countries are the basis of many statistical operations and policy making decisions at local level. This is a combinatorial optimisation problem defined as the partition of a given set of indivisible spatial units (covering a territory) into regions characterised by being (a) self-contained and (b) cohesive, in terms of spatial interaction data (flows, relationships). Usually, each region must reach a minimum size and self-containment level, and must be continuous. Although these optimisation problems have been typically solved through greedy methods, a recent strand of the literature in this field has been concerned with the use of evolutionary algorithms with ad hoc operators. Although these algorithms have proved to be successful in improving the results of some of the more widely applied official procedures, they are so time consuming that cannot be applied directly to solve real-world problems. In this paper we propose a new set of group-based mutation operators, featuring general operations over disjoint groups, tailored to ensure that all the constraints are respected during the operation to improve efficiency. A comparative analysis of our results with those from previous approaches shows that the proposed algorithm systematically improves them in terms of both quality and processing time, something of crucial relevance since it allows dealing with most large, real-world problems in reasonable time.

Hybrid simulation-optimization based approach for the optimal design of single-product biotechnological processes

In this work, we present a systematic method for the optimal development of bioprocesses that relies on the combined use of simulation packages and optimization tools. One of the main advantages of our method is that it allows for the simultaneous optimization of all the individual components of a bioprocess, including the main upstream and downstream units. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The primal dynamic optimization problem optimizes the operating conditions, bioreactor kinetics and equipment sizes, whereas the master levels entails the solution of a tailored mixed-integer linear programming (MILP) model that decides on the values of the integer variables (i.e., number of equipments in parallel and topological decisions). The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer® with an external NLP solver implemented in Matlab®. The capabilities of the proposed methodology are illustrated through its application to a typical fermentation process and to the production of the amino acid L-lysine.

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.

The Pliocene Mediterranean infilling of the Messinian Erosional Surface: New biostratigraphic data based on calcareous nannofossils (Bajo Segura Basin, SE Spain)

The Bajo Segura Basin (eastern Betic Cordillera) is a Mediterranean marginal basin where the Messinian Erosional Surface (MES), formed during the Messinian Salinity Crisis sea-level fall, is well developed. Overlying this major discontinuity the lower Pliocene transgressive sediments record the reflooding of the Mediterranean and the return to an open marine environment, the continental shelf being rebuilt after the Messinian erosion. The stratigraphic and biostratigraphic study of six sections allows two transgressive-regressive sequences filling the MES to be distinguished, correlated with the previously distinguished Mediterranean offshore seismic units. Ten calcareous nannofossil bioevents have been identified. The lower sequence can be dated according to nannofossil biozones NN12 to NN14 and the upper sequence by NN15 to NN16. The boundary between both lower Pliocene sedimentary sequences occur after the first common occurrence (FCO) of Discoaster asymmetricus found in the uppermost sediments of the lower sequence and before the first occurrence (FO) of Discoaster tamalis in the lowermost part of the upper sequence. Thus this sequence boundary can be estimated at between 4.1 and 4.0Ma ago.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.