908 resultados para Traffic control devices.


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Robotics is a field that presents a large number of problems because it depends on a large number of disciplines, devices, technologies and tasks. Its expansion from perfectly controlled industrial environments toward open and dynamic environment presents a many new challenges, such as robots household robots or professional robots. To facilitate the rapid development of robotic systems, low cost, reusability of code, its medium and long term maintainability and robustness are required novel approaches to provide generic models and software systems who develop paradigms capable of solving these problems. For this purpose, in this paper we propose a model based on multi-agent systems inspired by the human nervous system able to transfer the control characteristics of the biological system and able to take advantage of the best properties of distributed software systems.

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Robotics is an emerging field with great activity. Robotics is a field that presents several problems because it depends on a large number of disciplines, technologies, devices and tasks. Its expansion from perfectly controlled industrial environments toward open and dynamic environment presents a many new challenges. New uses are, for example, household robots or professional robots. To facilitate the low cost, rapid development of robotic systems, reusability of code, its medium and long term maintainability and robustness are required novel approaches to provide generic models and software systems who develop paradigms capable of solving these problems. For this purpose, in this paper we propose a model based on multi-agent systems inspired by the human nervous system able to transfer the control characteristics of the biological system and able to take advantage of the best properties of distributed software systems. Specifically, we model the decentralized activity and hormonal variation.

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For many years, humans and machines have shared the same physical space. To facilitate their interaction with humans, their social integration and for more rational behavior has been sought that the robots demonstrate human-like behavior. For this it is necessary to understand how human behavior is generated, discuss what tasks are performed and how relate to themselves, for subsequent implementation in robots. In this paper, we propose a model of competencies based on human neuroregulator system for analysis and decomposition of behavior into functional modules. Using this model allow separate and locate the tasks to be implemented in a robot that displays human-like behavior. As an example, we show the application of model to the autonomous movement behavior on unfamiliar environments and its implementation in various simulated and real robots with different physical configurations and physical devices of different nature. The main result of this work has been to build a model of competencies that is being used to build robotic systems capable of displaying behaviors similar to humans and consider the specific characteristics of robots.

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Humans and machines have shared the same physical space for many years. To share the same space, we want the robots to behave like human beings. This will facilitate their social integration, their interaction with humans and create an intelligent behavior. To achieve this goal, we need to understand how human behavior is generated, analyze tasks running our nerves and how they relate to them. Then and only then can we implement these mechanisms in robotic beings. In this study, we propose a model of competencies based on human neuroregulator system for analysis and decomposition of behavior into functional modules. Using this model allow separate and locate the tasks to be implemented in a robot that displays human-like behavior. As an example, we show the application of model to the autonomous movement behavior on unfamiliar environments and its implementation in various simulated and real robots with different physical configurations and physical devices of different nature. The main result of this study has been to build a model of competencies that is being used to build robotic systems capable of displaying behaviors similar to humans and consider the specific characteristics of robots.

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This layer is a georeferenced raster image of the historic paper map entitled: Traffic intensities, E. P. Richards. It was published by Calcutta Improvement Trust[?] in 1913. Scale [1:10,560]. Covers Kolkata and a portion of Hāora, India. The image inside the map neatline is georeferenced to the surface of the earth and fit to the 'WGS 1984 UTM Zone 45N' coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as roads with traffic density information, railroads, drainage, canals, selected buildings, fortification, docks, parks, and more. Includes note on traffic intensities and legend of vehicles per hour. This layer is part of a selection of digitally scanned and georeferenced historic maps from The Harvard Map Collection as part of the Imaging the Urban Environment project. Maps selected for this project represent major urban areas and cities of the world, at various time periods. These maps typically portray both natural and manmade features at a large scale. The selection represents a range of regions, originators, ground condition dates, scales, and purposes.

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Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application-level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word-embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of $ 3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohen's Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.

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Bio-logging studies suffer from the lack of real controls. However, it is still possible to compare indirect parameters between control and equipped animals to assess the level of global disturbance due to instrumentation. In addition, it is also possible to compare the behaviour of free-ranging animals between individuals equipped with different techniques or instruments to determine the less deleterious approach. We instrumented Adelie Penguins (Pygoscelis adeliae) with internal or external time-depth recorders and monitored them in parallel with a control group during the first foraging trip following instrumentation. Foraging trip duration was significantly longer in the internally-equipped group. This difference was due to a larger number of dives, reflecting a lower foraging ability or a higher food demand, and longer periods of recovery at the surface. These longer recovery periods were likely to be due to a reduced efficiency to ventilate at the surface, probably because the implanted devices pressurised adjacent organs such as air sacs. Moreover, descent and ascent rates were slightly lower in externally-equipped penguins, presumably because external instrumentation increased the bird drag. Looking at our results, implantation appears more disadvantageous - at least for short-term deployment - than external equipment in Adelie Penguins, while this method has been described to induce no negative effects in long-term studies. This underlines the need to control for potential effects due to methodological aspects in any study using data loggers in free-ranging animals, to minimise disturbance and collect reliable data.

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Federal Highway Administration, Office of Safety and Traffic Operations Research and Development, Washington, D.C.

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Federal Highway Administration, Traffic Systems Division, Washington, D.C.

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National Highway Traffic Safety Administration, Washington, D.C.

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National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.

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Federal Highway Administration, Office of Research, Development and Technology, Washington, D.C.

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Federal Highway Administration, Office of Research, Development and Technology, Washington, D.C.