282 research outputs found

    Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery

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    International audienceWe introduce a dynamical spatio-temporal model formalized as a recurrent neural network for forecasting time series of spatial processes, i.e. series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts the observations from the latent representations. We consider several variants of this model, corresponding to different prior hypothesis about the spatial relations between the series. The model is evaluated and compared to state-of-the-art baselines, on a variety of forecasting problems representative of different application areas: epidemiology, geo-spatial statistics and car-traffic prediction. Besides these evaluations, we also describe experiments showing the ability of this approach to extract relevant spatial relations

    Analysis of Trajectories by Preserving Structural Information

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    The analysis of trajectories from traffic data is an established and yet fast growing area of research in the related fields of Geo-analytics and Geographic Information Systems (GIS). It has a broad range of applications that impact lives of millions of people, e.g., in urban planning, transportation and navigation systems and localized search methods. Most of these applications share some underlying basic tasks which are related to matching, clustering and classification of trajectories. And, these tasks in turn share some underlying problems, i.e., dealing with the noisy and variable length spatio-temporal sequences in the wild. In our view, these problems can be handled in a better manner by exploiting the spatio-temporal relationships (or structural information) in sampled trajectory points that remain considerably unharmed during the measurement process. Although, the usage of such structural information has allowed breakthroughs in other fields related to the analysis of complex data sets [18], surprisingly, there is no existing approach in trajectory analysis that looks at this structural information in a unified way across multiple tasks. In this thesis, we build upon these observations and give a unified treatment of structural information in order to improve trajectory analysis tasks. This treatment explores for the first time that sequences, graphs, and kernels are common to machine learning and geo-analytics. This common language allows to pool the corresponding methods and knowledge to help solving the challenges raised by the ever growing amount of movement data by developing new analysis models and methods. This is illustrated in several ways. For example, we introduce new problem settings, distance functions and a visualization scheme in the area of trajectory analysis. We also connect the broad fild of kernel methods to the analysis of trajectories, and, we strengthen and revisit the link between biological sequence methods and analysis of trajectories. Finally, the results of our experiments show that - by incorporating the structural information - our methods improve over state-of-the-art in the focused tasks, i.e., map matching, clustering and traffic event detection

    Development of an intelligent earthwork optimization system

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    Tese de Doutoramento em Engenharia Civil.Earthworks are often regarded as one of the most costly and time-consuming components of linear infrastructure constructions (e.g., road, railway and airports). Since actual construction requirements originate higher demands for productivity and safety in earthwork constructions, the optimal usage of every resource in these tasks is paramount. The management of resources in an earthwork construction site is, in great part, a function of the allocation of the available equipment, for which there are a vast number of possible equipment allocation combinations. Simultaneously, while there is often high competitiveness, where the pressure is to provide the least possible costs and durations, contractors and project designers often settle for an allocation solution that is mostly based on their own intuition and accumulated experience. This guarantees neither optimal resource usage, nor a solution associated with minimal cost and duration. The optimal allocation of equipment in earthwork tasks is a complex problem that requires the study of several different aspects, as well as the knowledge of a large number of factors. In fact, earthworks are comprised by a combination of repetitive, sequential, and interdependent activities based on heavy mechanical equipment (i.e., resources), such as excavators, dumper trucks, bulldozers and compactors. In order to optimally allocate the available resources, knowledge regarding their specifications (e.g., capacity, weight, horsepower) and the work conditions to which they will be subjected (e.g., material types, required and available volumes in embankment and excavation fronts, respectively) is essential. This knowledge can be translated into the productivity (i.e., work rate) of each piece of equipment when working under a specific set of conditions. Moreover, since earthwork tasks are inherently sequential and interdependent, the interaction between the allocated equipment must be taken into account. A typical example of this is the need for matching the work rate of an excavator plant with the capacity of a truck plant to haul the excavated material to the embankment fronts. Given the non-trivial characteristics of the earthwork allocation problem, conventional Operation Research (e.g., linear programming) and blind search methods are infeasible. As such, a potential solution is to adopt metaheuristics – modern optimization methods capable of searching large space regions under a reasonable use of computational resources. While this may address the issue of optimizing such a complex problem, the lack of knowledge regarding optimization parameters under different work conditions, such as equipment productivity, calls for a different approach. Bearing in mind the availability of large databases, including in the earthworks area, that have been gathered in recent years by construction companies, technologies like data mining (DM) come forward as ideal tools for solving this problem. Indeed, the learning capabilities of DM algorithms can be applied to databases embodying the productivity of several equipment types when subjected to different work conditions. The extracted knowledge can then be used to estimate the productivity of the available equipment under similar work conditions. Furthermore, as previously referred, since earthwork tasks include the material hauling from excavation to embankment fronts, it also becomes imperative to analyse and optimize the possible transportation networks. In this context, the use of geographic information systems provides an easy method to study the possible trajectories for transportation equipment in a construction site, ultimately allowing for a choice of the best paths to improve the workflow. This work explores the integration of different technologies in order to allow for an optimization of the earthworks process. This is translated in the form of an evolutionary multi-criteria optimization system, capable of searching for the best allocation of the available equipment that minimizes a set of goals (e.g., cost, duration, environmental impact). The results stemming from the application of the system to a case study in a Portuguese earthwork construction site are presented. These comprise the assessment of the system performance, including a comparison between different optimization methods. Furthermore, an analysis regarding the improvement of workflow in the construction site after the implementation of the system is discussed, in the context of several comparisons between original (i.e., obtained by manual design) and optimized allocation solutions. Ultimately, these results illustrate the potential and importance of using this kind of technologies in the management and optimization of earthworks.Em projetos de construção de infraestruturas de transporte lineares (e.g., estradas, vias férreas e aeroportos), as terraplenagens são geralmente consideradas um dos componentes com custos e tempos de execução mais elevados. Tendo em conta que cada vez mais é exigido um aumento na produtividade e segurança no contexto das construções de terraplenagens, torna-se fulcral a otimização de todas as tarefas relacionadas com este processo. A gestão de recursos num estaleiro de terraplenagens é, em grande parte, função da alocação do equipamento mecânico disponível, para a qual existe um número quase infinito de soluções possíveis em cada caso. Simultaneamente, embora se verifique um alto nível de competitividade nesta área, onde o objetivo é obter custos e durações de execução o mais baixos possíveis, o planeamento das tarefas de terraplenagens é em grande parte baseado na experiência acumulada dos engenheiros e especialistas. Porém, tais métodos não garantem nem uma utilização ótima dos recursos disponíveis, nem uma solução associada ao custo e duração de execução mínimos. A alocação ótima de equipamento mecânico em tarefas de terraplenagens é um problema complexo que requer o estudo de vários aspectos distintos, assim como o conhecimento de um elevado número de fatores. De facto, estas tarefas são demarcadas por combinações de atividades repetitivas, fortemente baseadas no uso de equipamento mecânico (i.e., recursos), tal como escavadoras, dumpers, espalhadores e compactadores. Para que seja possível a sua alocação ótima, é essencial o conhecimento das suas especificações (e.g., capacidade, peso, potência) e das condições a que estão sujeitos durante a sua atividade (e.g., tipos de material, volumes disponíveis em frentes de escavação e necessários em frentes de aterro). Este conhecimento pode ser traduzido na produtividade de cada equipamento quando sujeito a determinadas condições de trabalho. Para além disso, uma vez que as terraplenagens consistem em tarefas inerentemente sequenciais e interdependentes, a interação entre os equipamentos tem de ser tomada em consideração. Um exemplo típico deste aspecto pode ser ilustrado pela necessidade de sincronizar a produtividade de uma equipa de escavadoras com a de uma equipa de dumpers, para que seja possível um fluxo constande de escavação e transporte de geomateriais das frentes de escavação para as frentes de aterro. Tendo em conta as características não triviais do problema de alocação em terraplenagens, os métodos convencionais de procura de soluções, tais como Investigação Operacional (e.g. programação linear) e busca exaustiva são impraticáveis. Assim, uma potencial solução é a adoção de metaheurísticas – métodos de otimização moderna capazes de efetuar a busca de soluções em espaços de procura extensos com níveis de exigência computacional razoáveis. Embora estes métodos sejam práticos para a otimização de problemas de elevado nível de complexidade, como é o caso das terraplenagens, existe ainda a necessidade de abordar o problema relacionado com a escassez de conhecimento de vários parâmetros necessários à otimização, tais como a produtividade dos equipamentos sujeitos a diferentes condições de trabalho. Considerando os recentes avanços da tecnologia e o aumento da prática de recolha de dados, verifica-se a disponibilidade de extensas bases de dados de construção, incluindo na área de terraplenagens. Neste sentido, tecnologias tais como o data mining (DM) surgem como ferramentas ideais para abordar esse problema. De fato, as capacidades de aprendizagem dos algoritmos de DM podem ser aplicadas às bases de dados existentes com informação relativa à produtividade de vários tipos de equipamento sujeitos a diferentes condições de trabalho. Mediante este processo, o conhecimento extraído pode então ser usado em novos casos para estimar a produtividade de equipamentos em condições semelhantes. Adicionalmente, uma vez que as tarefas de terraplenagens incluem o transporte de materiais de frentes de escavação para frentes de aterro, como previamente referido, torna-se ainda imperativa a análise e otimização das potenciais trajetórias de transporte ao longo do estaleiro. Neste contexto, a utilização de sistemas de informação geográficos providencia um método eficaz de estudo e escolha das melhores trajetórias para o equipamento de transporte, melhorando o fluxo de trabalho no estaleiro. Este trabalho explora a integração de diferentes tecnologias tendo em vista a otimização das tarefas de terraplenagens. Isto concretiza-se sob a forma de um sistema de otimização evolutiva multi-objetivo, capaz de eleger a melhor distribuição dos equipamentos de terraplenagens disponíveis que minimiza um determinado conjunto de objetivos (e.g., custo, duração, impacto ambiental). São apresentados os resultados decorrentes da aplicação do sistema desenvolvido num caso de estudo, associado a um estaleiro de terraplenagens em Portugal. Estes abrangem a avaliação do desempenho do sistema de otimização, incluindo a comparação de vários métodos de otimização. Para além disso, é realizada uma análise relativa ao melhoramento do fluxo de trabalho no estaleiro após a implementação do sistema, sendo enquadrada numa série de comparações entre as soluções originais (i.e., obtidas pelos métodos convencionais de dimensionamento) e as soluções otimizadas correspondentes. Em última análise, estes resultados ilustram o potencial e a importância da utilização deste tipo de tecnologias na gestão e otimização das terraplenagens.Fundação para a Ciência e a Tecnologia (FCT) SFRH/BD/71501/2010

    Aerial Vehicles

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    This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

    Advances in Robot Navigation

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    Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

    Complex queries and complex data

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    With the widespread availability of wearable computers, equipped with sensors such as GPS or cameras, and with the ubiquitous presence of micro-blogging platforms, social media sites and digital marketplaces, data can be collected and shared on a massive scale. A necessary building block for taking advantage from this vast amount of information are efficient and effective similarity search algorithms that are able to find objects in a database which are similar to a query object. Due to the general applicability of similarity search over different data types and applications, the formalization of this concept and the development of strategies for evaluating similarity queries has evolved to an important field of research in the database community, spatio-temporal database community, and others, such as information retrieval and computer vision. This thesis concentrates on a special instance of similarity queries, namely k-Nearest Neighbor (kNN) Queries and their close relative, Reverse k-Nearest Neighbor (RkNN) Queries. As a first contribution we provide an in-depth analysis of the RkNN join. While the problem of reverse nearest neighbor queries has received a vast amount of research interest, the problem of performing such queries in a bulk has not seen an in-depth analysis so far. We first formalize the RkNN join, identifying its monochromatic and bichromatic versions and their self-join variants. After pinpointing the monochromatic RkNN join as an important and interesting instance, we develop solutions for this class, including a self-pruning and a mutual pruning algorithm. We then evaluate these algorithms extensively on a variety of synthetic and real datasets. From this starting point of similarity queries on certain data we shift our focus to uncertain data, addressing nearest neighbor queries in uncertain spatio-temporal databases. Starting from the traditional definition of nearest neighbor queries and a data model for uncertain spatio-temporal data, we develop efficient query mechanisms that consider temporal dependencies during query evaluation. We define intuitive query semantics, aiming not only at returning the objects closest to the query but also their probability of being a nearest neighbor. After theoretically evaluating these query predicates we develop efficient querying algorithms for the proposed query predicates. Given the findings of this research on nearest neighbor queries, we extend these results to reverse nearest neighbor queries. Finally we address the problem of querying large datasets containing set-based objects, namely image databases, where images are represented by (multi-)sets of vectors and additional metadata describing the position of features in the image. We aim at reducing the number of kNN queries performed during query processing and evaluate a modified pipeline that aims at optimizing the query accuracy at a small number of kNN queries. Additionally, as feature representations in object recognition are moving more and more from the real-valued domain to the binary domain, we evaluate efficient indexing techniques for binary feature vectors.Nicht nur durch die Verbreitung von tragbaren Computern, die mit einer Vielzahl von Sensoren wie GPS oder Kameras ausgestattet sind, sondern auch durch die breite Nutzung von Microblogging-Plattformen, Social-Media Websites und digitale Marktplätze wie Amazon und Ebay wird durch die User eine gigantische Menge an Daten veröffentlicht. Um aus diesen Daten einen Mehrwert erzeugen zu können bedarf es effizienter und effektiver Algorithmen zur Ähnlichkeitssuche, die zu einem gegebenen Anfrageobjekt ähnliche Objekte in einer Datenbank identifiziert. Durch die Allgemeinheit dieses Konzeptes der Ähnlichkeit über unterschiedliche Datentypen und Anwendungen hinweg hat sich die Ähnlichkeitssuche zu einem wichtigen Forschungsfeld, nicht nur im Datenbankumfeld oder im Bereich raum-zeitlicher Datenbanken, sondern auch in anderen Forschungsgebieten wie dem Information Retrieval oder dem Maschinellen Sehen entwickelt. In der vorliegenden Arbeit beschäftigen wir uns mit einem speziellen Anfrageprädikat im Bereich der Ähnlichkeitsanfragen, mit k-nächste Nachbarn (kNN) Anfragen und ihrem Verwandten, den Revers k-nächsten Nachbarn (RkNN) Anfragen. In einem ersten Beitrag analysieren wir den RkNN Join. Obwohl das Problem von reverse nächsten Nachbar Anfragen in den letzten Jahren eine breite Aufmerksamkeit in der Forschungsgemeinschaft erfahren hat, wurde das Problem eine Menge von RkNN Anfragen gleichzeitig auszuführen nicht ausreichend analysiert. Aus diesem Grund formalisieren wir das Problem des RkNN Joins mit seinen monochromatischen und bichromatischen Varianten. Wir identifizieren den monochromatischen RkNN Join als einen wichtigen und interessanten Fall und entwickeln entsprechende Anfragealgorithmen. In einer detaillierten Evaluation vergleichen wir die ausgearbeiteten Verfahren auf einer Vielzahl von synthetischen und realen Datensätzen. Nach diesem Kapitel über Ähnlichkeitssuche auf sicheren Daten konzentrieren wir uns auf unsichere Daten, speziell im Bereich raum-zeitlicher Datenbanken. Ausgehend von der traditionellen Definition von Nachbarschaftsanfragen und einem Datenmodell für unsichere raum-zeitliche Daten entwickeln wir effiziente Anfrageverfahren, die zeitliche Abhängigkeiten bei der Anfragebearbeitung beachten. Zu diesem Zweck definieren wir Anfrageprädikate die nicht nur die Objekte zurückzugeben, die dem Anfrageobjekt am nächsten sind, sondern auch die Wahrscheinlichkeit mit der sie ein nächster Nachbar sind. Wir evaluieren die definierten Anfrageprädikate theoretisch und entwickeln effiziente Anfragestrategien, die eine Anfragebearbeitung zu vertretbaren Laufzeiten gewährleisten. Ausgehend von den Ergebnissen für Nachbarschaftsanfragen erweitern wir unsere Ergebnisse auf Reverse Nachbarschaftsanfragen. Zuletzt behandeln wir das Problem der Anfragebearbeitung bei Mengen-basierten Objekten, die zum Beispiel in Bilddatenbanken Verwendung finden: Oft werden Bilder durch eine Menge von Merkmalsvektoren und zusätzliche Metadaten (zum Beispiel die Position der Merkmale im Bild) dargestellt. Wir evaluieren eine modifizierte Pipeline, die darauf abzielt, die Anfragegenauigkeit bei einer kleinen Anzahl an kNN-Anfragen zu maximieren. Da reellwertige Merkmalsvektoren im Bereich der Objekterkennung immer öfter durch Bitvektoren ersetzt werden, die sich durch einen geringeren Speicherplatzbedarf und höhere Laufzeiteffizienz auszeichnen, evaluieren wir außerdem Indexierungsverfahren für Binärvektoren

    SLS: Smart localization service: human mobility models and machine learning enhancements for mobile phone’s localization

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    In recent years we are witnessing a noticeable increment in the usage of new generation smartphones, as well as the growth of mobile application development. Today, there is an app for almost everything we need. We are surrounded by a huge number of proactive applications, which automatically provide relevant information and services when and where we need them. This switch from the previous generation of passive applications to the new one of proactive applications has been enabled by the exploitation of context information. One of the most important and most widely used pieces of context information is location data. For this reason, new generation devices include a localization engine that exploits various embedded technologies (e.g., GPS, WiFi, GSM) to retrieve location information. Consequently, the key issue in localization is now the efficient use of the mobile localization engine, where efficient means lightweight on device resource consumption, responsive, accurate and safe in terms of privacy. In fact, since the device resources are limited, all the services running on it have to manage their trade-off between consumption and reliability to prevent a premature depletion of the phone’s battery. In turn, localization is one of the most demanding services in terms of resource consumption. In this dissertation I present an efficient localization solution that includes, in addition to the standard location tracking techniques, the support of other technologies already available on smartphones (e.g., embedded sensors), as well as the integration of both Human Mobility Modelling (HMM) and Machine Learning (ML) techniques. The main goal of the proposed solution is the provision of a continuous tracking service while achieving a sizeable reduction of the energy impact of the localization with respect to standard solutions, as well as the preservation of user privacy by avoiding the use of a back-end server. This results in a Smart Localization Service (SLS), which outperforms current solutions implemented on smartphones in terms of energy consumption (and, therefore, mobile device lifetime), availability of location information, and network traffic volume

    Circles within spirals, wheels within wheels; Body rotation facilitates critical insights into animal behavioural ecology

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    How animals behave is fundamental to enhancing their lifetime fitness, so defining how animals move in space and time relates to many ecological questions, including resource selection, activity budgets and animal movement networks. Historically, animal behaviour and movement has been defined by direct observation, however recent advancements in biotelemetry have revolutionised how we now assess behaviour, particularly allowing animals to be monitored when they cannot be seen. Studies now pair ‘convectional’ radio telemetries with motion sensors to facilitate more detailed investigations of animal space-use. Motion sensitive tags (containing e.g., accelerometers and magnetometers) provide precise data on body movements which characterise behaviour, and this has been exemplified in extensive studies using accelerometery data, which has been linked to space-use defined by GPS. Conversely, consideration of body rotation (particularly change in yaw) is virtually absent within the biologging literature, even though various scales of yaw rotation can reveal important patterns in behaviour and movement, with animal heading being a fundamental component characterising space-use. This thesis explores animal body angles, particularly about the yaw axis, for elucidating animal movement ecology. I used five model species (a reptile, a mammal and three birds) to demonstrate the value of assessing body rotation for investigating fine-scale movement-specific behaviours. As part of this, I advanced the ‘dead-reckoning’ method, where fine-scale animal movement between temporally poorly resolved GPS fixes can be deduced using heading vectors and speed. I addressed many issues with this protocol, highlighting errors and potential solutions but was able to show how this approach leads to insights into many difficult-to-study animal behaviours. These ranged from elucidating how and where lions cross supposedly impermeable man-made barriers to examining how penguins react to tidal currents and then navigate their way to their nests far from the sea in colonies enclosed within thick vegetation

    Automatic object classification for surveillance videos.

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    PhDThe recent popularity of surveillance video systems, specially located in urban scenarios, demands the development of visual techniques for monitoring purposes. A primary step towards intelligent surveillance video systems consists on automatic object classification, which still remains an open research problem and the keystone for the development of more specific applications. Typically, object representation is based on the inherent visual features. However, psychological studies have demonstrated that human beings can routinely categorise objects according to their behaviour. The existing gap in the understanding between the features automatically extracted by a computer, such as appearance-based features, and the concepts unconsciously perceived by human beings but unattainable for machines, or the behaviour features, is most commonly known as semantic gap. Consequently, this thesis proposes to narrow the semantic gap and bring together machine and human understanding towards object classification. Thus, a Surveillance Media Management is proposed to automatically detect and classify objects by analysing the physical properties inherent in their appearance (machine understanding) and the behaviour patterns which require a higher level of understanding (human understanding). Finally, a probabilistic multimodal fusion algorithm bridges the gap performing an automatic classification considering both machine and human understanding. The performance of the proposed Surveillance Media Management framework has been thoroughly evaluated on outdoor surveillance datasets. The experiments conducted demonstrated that the combination of machine and human understanding substantially enhanced the object classification performance. Finally, the inclusion of human reasoning and understanding provides the essential information to bridge the semantic gap towards smart surveillance video systems

    Benelux meeting on systems and control, 23rd, March 17-19, 2004, Helvoirt, The Netherlands

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