FE modelling of the Streicker Footbridge

The Streicker footbridge was completed in 2010 at the Princeton University Campus, over the Washington Road. It is about 104 m long and consists of a central main span supported by a steel arch and four lateral approaching legs. The deck is a post-tensioned high-performance concrete girder. Steel columns with “Y” shape support four lateral legs that connect the bridge to the lateral bearings on the ground and the whole system results a slender varying cross section main girder. The original shape in the horizontal plane provides horizontal stability to the footbridge despite the intrinsic slenderness of the steel supporting columns. Vertical stability is provided also by the arch in the central main-span and by the supporting columns under the legs. Cross section width increases from the midpoint of the main span to the connections with the legs and then remains constant up to the ground bearings. This work is focused on the development of a finite element analysis of the footbridge at different levels of refinement from the essential implementation of beam elements to more refined FE solutions for the prestressed concrete deck. The models are identified with respect to the available operational modal parameters. This deck discretization could further allow simulating the motion of a running/walking pedestrian along different trajectories

Multi‑Agent Foraging: state‑of‑the‑art and research challenges

International audienceThe foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth. First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems. Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic

The Gamification of Crowdsourcing Systems: Empirical Investigations and Design

Recent developments in modern information and communication technologies have spawned two rising phenomena, gamification and crowdsourcing, which are increasingly being combined into gamified crowdsourcing systems. While a growing number of organizations employ crowdsourcing as a way to outsource tasks related to the inventing, producing, funding, or distributing of their products and services to the crowd – a large group of people reachable via the internet – crowdsourcing initiatives become enriched with design features from games to motivate the crowd to participate in these efforts. From a practical perspective, this combination seems intuitively appealing, since using gamification in crowdsourcing systems promises to increase motivations, participation and output quality, as well as to replace traditionally used financial incentives. However, people in large groups all have individual interests and motivations, which makes it complex to design gamification approaches for crowds. Further, crowdsourcing systems exist in various forms and are used for various tasks and problems, thus requiring different incentive mechanisms for different crowdsourcing types. The lack of a coherent understanding of the different facets of gamified crowdsourcing systems and the lack of knowledge about the motivational and behavioral effects of applying various types of gamification features in different crowdsourcing systems inhibit us from designing solutions that harness gamification’s full potential. Further, previous research canonically uses competitive gamification, although crowdsourcing systems often strive to produce cooperative outcomes. However, the potentially relevant field of cooperative gamification has to date barely been explored. With a specific focus on these shortcomings, this dissertation presents several studies to advance the understanding of using gamification in crowdsourcing systems

Enhanced context-aware framework for individual and crowd condition prediction

Context-aware framework is basic context-aware that utilizes contexts such as user with their individual activities, location and time, which are hidden information derived from smartphone sensors. These data are used to monitor a situation in a crowd scenario. Its application using embedded sensors has the potential to monitor tasks that are practically complicated to access. Inaccuracies observed in the individual activity recognition (IAR) due to faulty accelerometer data and data classification problem have led to its inefficiency when used for prediction. This study developed a solution to this problem by introducing a method of feature extraction and selection, which provides a higher accuracy by selecting only the relevant features and minimizing false negative rate (FNR) of IAR used for crowd condition prediction. The approach used was the enhanced context-aware framework (EHCAF) for the prediction of human movement activities during an emergency. Three new methods to ensure high accuracy and low FNR were introduced. Firstly, an improved statistical-based time-frequency domain (SBTFD) representing and extracting hidden context information from sensor signals with improved accuracy was introduced. Secondly, a feature selection method (FSM) to achieve improved accuracy with statistical-based time-frequency domain (SBTFD) and low false negative rate was used. Finally, a method for individual behaviour estimation (IBE) and crowd condition prediction in which the threshold and crowd density determination (CDD) was developed and used, achieved a low false negative rate. The approach showed that the individual behaviour estimation used the best selected features, flow velocity estimation and direction to determine the disparity value of individual abnormality behaviour in a crowd. These were used for individual and crowd density determination evaluation in terms of inflow, outflow and crowd turbulence during an emergency. Classifiers were used to confirm features ability to differentiate individual activity recognition data class. Experimenting SBTFD with decision tree (J48) classifier produced a maximum of 99:2% accuracy and 3:3% false negative rate. The individual classes were classified based on 7 best features, which produced a reduction in dimension, increased accuracy to 99:1% and had a low false negative rate (FNR) of 2:8%. In conclusion, the enhanced context-aware framework that was developed in this research proved to be a viable solution for individual and crowd condition prediction in our society

Ecotoxicity of Plastics from Informal Waste Electric and Electronic Treatment and Recycling

Plastic materials account for about 20% of waste electrical and electronic equipment (WEEE). The recycling of this plastic fraction is a complex issue, heavily conditioned by the content of harmful additives, such as brominated flame retardants. Thus, the management and reprocessing of WEEE plastics pose environmental and human health concerns, mainly in developing countries, where informal recycling and disposal are practiced. The objective of this study was twofold. Firstly, it aimed to investigate some of the available options described in the literature for the re-use of WEEE plastic scraps in construction materials, a promising recycling route in the developing countries. Moreover, it presents an evaluation of the impact of these available end-of-life scenarios on the environment by means of the life cycle assessment (LCA) approach. In order to consider worker health and human and ecological risks, the LCA analysis focuses on ecotoxicity more than on climate change. The LCA evaluation confirmed that the plastic re-use in the construction sector has a lower toxicity impact on the environment and human health than common landfilling and incineration practices. It also shows that the unregulated handling and dismantling activities, as well as the re-use practices, contribute significantly to the impact of WEEE plastic treatments

Cloud-Enhanced Robotic System for Smart City Crowd Control

Cloud robotics in smart cities is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with a cloud computing infrastructure. It complements the Internet of Things (IoT) by creating an expanded network where robots offload data-intensive computation to the ubiquitous cloud to ensure quality of service (QoS). However, offloading for robots is significantly complex due to their unique characteristics of mobility, skill-learning, data collection, and decision-making capabilities. In this paper, a generic cloud robotics framework is proposed to realize smart city vision while taking into consideration its various complexities. Specifically, we present an integrated framework for a crowd control system where cloud-enhanced robots are deployed to perform necessary tasks. The task offloading is formulated as a constrained optimization problem capable of handling any task flow that can be characterized by a Direct Acyclic Graph (DAG).We consider two scenarios of minimizing energy and time, respectively, and develop a genetic algorithm (GA)-based approach to identify the optimal task offloading decisions. The performance comparison with two benchmarks shows that our GA scheme achieves desired energy and time performance. We also show the adaptability of our algorithm by varying the values for bandwidth and movement. The results suggest their impact on offloading. Finally, we present a multi-task flow optimal path sequence problem that highlights how the robot can plan its task completion via movements that expend the minimum energy. This integrates path planning with offloading for robotics. To the best of our knowledge, this is the first attempt to evaluate cloud-based task offloading for a smart city crowd control system

(MASSA: Multi-agent system to support functional annotation)

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 23-11-2015Predecir la función biológica de secuencias de Ácido Desoxirribonucleico (ADN) es unos de los mayores desafíos a los que se enfrenta la Bioinformática. Esta tarea se denomina anotación funcional y es un proceso complejo, laborioso y que requiere mucho tiempo. Dado su impacto en investigaciones y anotaciones futuras, la anotación debe ser lo más able y precisa posible. Idealmente, las secuencias deberían ser estudiadas y anotadas manualmente por un experto, garantizando así resultados precisos y de calidad. Sin embargo, la anotación manual solo es factible para pequeños conjuntos de datos o genomas de referencia. Con la llegada de las nuevas tecnologías de secuenciación, el volumen de datos ha crecido signi cativamente, haciendo aún más crítica la necesidad de implementaciones automáticas del proceso. Por su parte, la anotación automática es capaz de manejar grandes cantidades de datos y producir un análisis consistente. Otra ventaja de esta aproximación es su rapidez y bajo coste en relación a la manual. Sin embargo, sus resultados son menos precisos que los manuales y, en general, deben ser revisados ( curados ) por un experto. Aunque los procesos colaborativos de la anotación en comunidad pueden ser utilizados para reducir este cuello de botella, los esfuerzos en esta línea no han tenido hasta ahora el éxito esperado. Además, el problema de la anotación, como muchos otros en el dominio de la Bioinformática, abarca información heterogénea, distribuida y en constante evolución. Una posible aproximación para superar estos problemas consiste en cambiar el foco del proceso de los expertos individuales a su comunidad, y diseñar las herramientas de manera que faciliten la gestión del conocimiento y los recursos. Este trabajo adopta esta línea y propone MASSA (Multi-Agent System to Support functional Annotation), una arquitectura de Sistema Multi-Agente (SMA) para Soportar la Anotación funcional...Predicting the biological function of Deoxyribonucleic Acid (DNA) sequences is one of the many challenges faced by Bioinformatics. This task is called functional annotation, and it is a complex, labor-intensive, and time-consuming process. This annotation has to be as accurate and reliable as possible given its impact in further researches and annotations. In order to guarantee a high-quality outcome, each sequence should be manually studied and annotated by an expert. Although desirable, the manual annotation is only feasible for small datasets or reference genomes. As the volume of genomic data has been increasing, specially after the advent of Next Generation Sequencing techniques, automatic implementations of this process are a necessity. The automatic annotation can handle a huge amount of data and produce consistent analyses. Besides, it is faster and less expensive than the manual approach. However, its outcome is less precise than the one predicted manually and often has to be curated by an expert. Although collaborative processes of community annotation could address this expert bottleneck in automatic annotation, these e orts have failed until now. Moreover, the annotation problem, as many others in this domain, has to deal with heterogeneous information that is distributed and constantly evolving. A possible way to overcome these hurdles is with a shift in the focus of the process from individual experts to communities, and with a design of tools that facilitates the management of knowledge and resources. This work follows this approach proposing MASSA, an architecture for a Multi-Agent System (MAS) to Support functional Annotation...Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications