140 research outputs found

    Travel Package Recommendation

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    Location Based SocialNetworks (LBSN) benefit the users by allowing them to share their locations and life moments with their friends. The users can also review the locations they have visited. Classical recommender systems provide users a ranked list of single items. This is not suitable for applications like trip planning,where the recommendations should contain multiple items in an appropriate sequence. The problem of generating such recommendations is challenging due to various critical aspects, which includes user interest, budget constraints and high sparsity in the available data used to solve the problem. In this paper, we propose a graph based approach to recommend a set of personalized travel packages. Each recommended package comprises of a sequence of multiple Point of Interests (POIs). Given the current location and spatio-temporal constraints, our goal is to recommend a package which satisfies the constraints. This approach utilizes the data collected fromLBSNs to learn user preferences and also models the location popularity

    Emergency rapid mapping with drones: models and solution approaches for offline and online mission planning

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    Die Verfügbarkeit von unbemannten Luftfahrzeugen (unmanned aerial vehicles oder UAVs) und die Fortschritte in der Entwicklung leichtgewichtiger Sensorik eröffnen neue Möglichkeiten für den Einsatz von Fernerkundungstechnologien zur Schnellerkundung in Großschadenslagen. Hier ermöglichen sie es beispielsweise nach Großbränden, Einsatzkräften in kurzer Zeit ein erstes Lagebild zur Verfügung zu stellen. Die begrenzte Flugdauer der UAVs wie auch der Bedarf der Einsatzkräfte nach einer schnellen Ersteinschätzung bedeuten jedoch, dass die betroffenen Gebiete nur stichprobenartig überprüft werden können. In Kombination mit Interpolationsverfahren ermöglichen diese Stichproben anschließend eine Abschätzung der Verteilung von Gefahrstoffen. Die vorliegende Arbeit befasst sich mit dem Problem der Planung von UAV-Missionen, die den Informationsgewinn im Notfalleinsatz maximieren. Das Problem wird dabei sowohl in der Offline-Variante, die Missionen vor Abflug bestimmt, als auch in der Online-Variante, bei der die Pläne während des Fluges der UAVs aktualisiert werden, untersucht. Das übergreifende Ziel ist die Konzeption effizienter Modelle und Verfahren, die Informationen über die räumliche Korrelation im beobachteten Gebiet nutzen, um in zeitkritischen Situationen Lösungen von hoher Vorhersagegüte zu bestimmen. In der Offline-Planung wird das generalized correlated team orienteering problem eingeführt und eine zweistufige Heuristik zur schnellen Bestimmung explorativer UAV-Missionen vorgeschlagen. In einer umfangreichen Studie wird die Leistungsfähigkeit und Konkurrenzfähigkeit der Heuristik hinsichtlich Rechenzeit und Lösungsqualität bestätigt. Anhand von in dieser Arbeit neu eingeführten Benchmarkinstanzen wird der höhere Informationsgewinn der vorgeschlagenen Modelle im Vergleich zu verwandten Konzepten aufgezeigt. Im Bereich der Online-Planung wird die Kombination von lernenden Verfahren zur Modellierung der Schadstoffe mit Planungsverfahren, die dieses Wissen nutzen, um Missionen zu verbessern, untersucht. Hierzu wird eine breite Spanne von Lösungsverfahren aus unterschiedlichen Disziplinen klassifiziert und um neue effiziente Modellierungsvarianten für die Schnellerkundung ergänzt. Die Untersuchung im Rahmen einer ereignisdiskreten Simulation zeigt, dass vergleichsweise einfache Approximationen räumlicher Zusammenhänge in sehr kurzer Zeit Lösungen hoher Qualität ermöglichen. Darüber hinaus wird die höhere Robustheit genauerer, aber aufwändigerer Modelle und Lösungskonzepte demonstriert

    Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level

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    Energy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.Part of this work has been developed from results obtained during the H2020 “Optimised Energy Efficient Design Platform for Refurbishment at District Level” (OptEEmAL) project, Grant No. 680676

    Recommending personalized schedules in urban environments

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    A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

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    Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence

    The Southeastern Librarian v. 60, no. 1 (Spring 2012) Complete Issue

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    Complete issue of The Southeastern Librarian, volume 60, No. 1 (Spring 2012)

    Communication risk and strategy in temporary organizations

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    Communication is a critical and emerging metric for successful outcomes in the high-stakes field of project management. Professional management societies have quantified financial losses caused by ineffective communication. Consulting project management exemplifies a maximum communication risk environment - misunderstanding threatens project finances, strict deadlines, and technical benchmarks - exacerbated by the complexity of a temporary organization structure. The context of work in a temporary organization adds layers of ambiguity to project communications - an ill-structured domain in technical communication terms. Formal study of communication in temporary organizations is relatively new. Recent studies are derived from engineering and business management perspectives. This baseline study investigates risk and strategy in temporary organizations from a communication perspective. Project management consultants dialogue about their experiences of project risk and communication strategy in a critical incident interview. This research identifies the communication complexities of work in these temporary contexts. Contrasting the base communication models of professional project management, this study proposes rhetorical analysis as a systems thinking strategy for project communication. This thesis argues that professional technical communication is strategic expertise and advocates humanistic strategies to mitigate the elevated sociotechnical communication risk within a temporary organization

    Optimized routing of unmanned aerial systems to address informational gaps in counterinsurgency

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    Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 129-132).Recent military conflicts reveal that the ability to assess and improve the health of a society contributes more to a successful counterinsurgency (COIN) than direct military engagement. In COIN, a military commander requires maximum situational awareness not only with regard to the enemy but also to the status of logistical support concerning civil security operations, governance, essential services, economic development, and the host nation's security forces. Although current Brigade level Unmanned Aerial Systems (UAS) can provide critical unadulterated views of progress with respect to these Logistical Lines of Operation (LLO), the majority of units continue to employ UASs for strictly conventional combat support missions. By incorporating these LLO targets into the mission planning cycle with a collective UAS effort, commanders can gain a decisive advantage in COIN. Based on the type of LLO, some of these targets might require more than a single observation to provide the maximum benefit. This thesis explores an integer programming and metaheuristic approach to solve the Collective UAS Planning Problem (CUPP). The solution to this problem provides optimal plans for multiple sortie routes for heterogeneous UAS assets that collectively visit these diverse secondary LLO targets while in transition to or from primary mission targets. By exploiting the modularity of the Raven UAS asset, we observe clear advantages, with respect to the total number of targets observed and the total mission time, from an exchange of Raven UASs and from collective sharing of targets between adjacent units. Comparing with the status quo of decentralized operations, we show that the results of this new concept demonstrate significant improvements in target coverage. Furthermore, the use of metaheuristics with a Repeated Local Search algorithm facilitates the fast generation of solutions, each within 1.72% of optimality for problems with up to 5 UASs and 25 nodes. By adopting this new paradigm of collective Raven UAS operations and LLO integration, Brigade level commanders can maximize the use of organic UAS assets to address the complex information requirements characteristic of COIN. Future work for the CUPP to reflect a more realistic model could include the effects of random service times and high priority pop-up targets during mission execution.by Andrew C. Lee.S.M.in Transportatio

    Risk Minimization for Spreading Processes over Networks via Surveillance Scheduling and Sparse Control

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    Spreading processes, such as epidemics and wildfires, have an initial localized outbreak that spreads rapidly throughout a network. The real-world risks associated with such events have stressed the importance and current limitations of methods to quickly map and monitor outbreaks and to reduce their impact by planning appropriate intervention strategies. This thesis is, therefore, concerned with risk minimization of spreading processes over networks via surveillance scheduling and sparse control. This is achieved by providing a flexible optimization framework that combines surveillance and intervention to minimize the risk. Here, risk is defined as the product of the probability of an outbreak occurring and the future impact of that outbreak. The aim is now to bound or minimize the risk by allocation of resources and use of persistent monitoring schedules. When setting up an optimization framework, four other aspects have been found to be of importance. First of all, being able to provide targeted risk estimation and minimization for more vulnerable or high cost areas. Second and third, scalability of algorithms and sparsity of resource allocation are essential due to the large network structures. Finally, for wildfires specifically, there is a gap between the information embedded in fire propagation models and utilizing it for path planning algorithms for efficient remote sensing. The presented framework utilizes the properties of positive systems and convex optimization, in particular exponential cone programming, to provide flexible and scalable algorithms for both surveillance and intervention purposes. We demonstrate with different spreading process examples and scenarios, focusing on epidemics and wildfires, that the presented framework gives convincing and scalable results. In particular, we demonstrate how our method can include persistent monitoring scenarios and provide more targeted and sparse resource allocation compared to previous approaches
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