3,687 research outputs found

    Position paper on realizing smart products: challenges for Semantic Web technologies

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    In the rapidly developing space of novel technologies that combine sensing and semantic technologies, research on smart products has the potential of establishing a research field in itself. In this paper, we synthesize existing work in this area in order to define and characterize smart products. We then reflect on a set of challenges that semantic technologies are likely to face in this domain. Finally, in order to initiate discussion in the workshop, we sketch an initial comparison of smart products and semantic sensor networks from the perspective of knowledge technologies

    A statistical approach for estimating mean maximum urban temperature excess.

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    Munkánkban a városi hősziget (UHI) maximális napi kifejlődését vizsgáltuk Szegeden, a beépítettségi paraméterek függvényében. A hőmérsékleti adatok valamint a beépítettségi arány, a vízfelszín-arány, az égbolt láthatósági index és az épületmagasság, valamint ezek területi kiterjesztései közötti kapcsolatot statisztikus modellezéssel határoztuk meg. A kapott modell-egyenleteket mindkét félévre (fűtési és nem-fűtési) többváltozós lineáris regresszió segítségével állapítottuk meg. Az eredményekből világosan látszik, hogy szignifikáns kapcsolat mutatható ki a maximális UHI területi eloszlása és a beépítettségi paraméterek között, ami azt jelenti, hogy e tényezők fontos szerepet jatszanak a városi hőmérsékleti többlet területi eloszlásának kialakításában. A városi paraméterek közül az égbolt láthatósági index és az épületmagasság a leginkább meghatározó tényező, ami összhangban van a városi felszín energia-egyenlegével. | Investigations concentrated on the urban heat island (UHI) in its strongest development during the diurnal cycle in Szeged, Hungary. Task includes development of statistical models in the heating and non-heating seasons using urban surface parameters (built-up and water surface ratios, sky view factor, building height) and their areal extensions. Model equations were determined by means of stepwise multiple linear regression analysis. As the results show, there is a clear connection between the spatial distribution of the UHI and the examined parameters, so these parameters play an important role in the evolution of the UHI intensity field. Among them the sky view factor and the building height are the most determining factors, which are in line with the urban surface energy balance

    T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data

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    The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the problem, and extend the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences. The temporal complexity of the T-pattern approach is prohibitive in the scenarios we consider. We remedy this with a statistical model to obtain a fast and robust algorithm to find patterns in temporal data. We test our algorithm on a recent database collected with passive infrared sensors with millions of events

    Volcanic Ash Retrieval Using a New Geostationary Satellite

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    Human Action Recognition and Monitoring in Ambient Assisted Living Environments

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    Population ageing is set to become one of the most significant challenges of the 21st century, with implications for almost all sectors of society. Especially in developed countries, governments should immediately implement policies and solutions to facilitate the needs of an increasingly older population. Ambient Intelligence (AmI) and in particular the area of Ambient Assisted Living (AAL) offer a feasible response, allowing the creation of human-centric smart environments that are sensitive and responsive to the needs and behaviours of the user. In such a scenario, understand what a human being is doing, if and how he/she is interacting with specific objects, or whether abnormal situations are occurring is critical. This thesis is focused on two related research areas of AAL: the development of innovative vision-based techniques for human action recognition and the remote monitoring of users behaviour in smart environments. The former topic is addressed through different approaches based on data extracted from RGB-D sensors. A first algorithm exploiting skeleton joints orientations is proposed. This approach is extended through a multi-modal strategy that includes the RGB channel to define a number of temporal images, capable of describing the time evolution of actions. Finally, the concept of template co-updating concerning action recognition is introduced. Indeed, exploiting different data categories (e.g., skeleton and RGB information) improve the effectiveness of template updating through co-updating techniques. The action recognition algorithms have been evaluated on CAD-60 and CAD-120, achieving results comparable with the state-of-the-art. Moreover, due to the lack of datasets including skeleton joints orientations, a new benchmark named Office Activity Dataset has been internally acquired and released. Regarding the second topic addressed, the goal is to provide a detailed implementation strategy concerning a generic Internet of Things monitoring platform that could be used for checking users' behaviour in AmI/AAL contexts
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