1,721,083 research outputs found
A probabilistic representation for the solutions to some non-linear PDEs using pruned branching trees
Full text linkThe solutions to a large class of non-linear parabolic PDEs are given in terms of expectations of suitable functionals of a tree of branching particles. A sufficient, and in some cases necessary, condition is given for the integrability of the stochastic representation, using a comparison scalar PDE.
In cases where the representation fails to be integrable, a sequence of pruned trees is constructed, producing approximate stochastic representations that in some cases converge, globally in time, to the solution of the original PDE. (c) 2006 Elsevier Masson SAS
Action Planning for the collision Avoidance System Using Neural Networks
No full textAn understanding of the scenario in complex traffic situations is essential in order to give an early warning, or in an autonomous system, to intervene in the urban or motorway environment. A collision avoidance system needs both to predict possible collisions or hazards and to plan a less hazardous move in a critical situation. A crucial factor in the success of the system is the use of a priori knowledge. The classical problem with a knowledge-based decision making system is the acquisition and representation of the knowledge. It is difficult to design and develop a system for real time auto-piloting in varied traffic environments. Neural networks are ideally suited for applications where a large training set is available because they can apply human decision making criteria in different situations. The learning processes encapsulate a wide variety of drivers' reactions to various scenarios. Neural networks' abilities to generalise their training to new scenarios in the light of driving experience and to make emotion-free decisions leads to a system that is adaptive and closely which resembles human action strategy. Recognition of a scenario is achieved by acquiring data about a scene from a variety of sensors. Visual data is preprocessed and features are extracted using a real-time image processing system, while microwave radar provides obstacle information and distances. This paper described an early warning system and suggests possible responses to various traffic situations. The paper focuses on various learning algorithms for decision making which is based on the current model and immediate history only. It would help if we could always recognise the dominant threat at every instant and avoid it by either slowing down or changing direction. In our analysis of situations using neural networks, the test cases show that reasonably such behaviour can be generated. In order to validate the auto pilot it is tested in parallel with expert drivers to assess the drivers' action in a number of scenarios. The network's intervention control is verified by independent observers. The intervention strategies are based on a number of rules by which an intervention controller is trained to generate various actions. These rules are fine tuned on-line to achieve reliable and repeatable actions
Aspects of Neural Networks in Intelligent Collision Avoidance Systems for Prometheus
No full textThis paper presents our work on adaptive driver modelling and obstacle classification applications, which will be incorporated into an intelligent collision avoidance system (ICAS) for road vehicles. The reliability of the ICAS is largely determined by the accuracy of these models. Multi-layered-Perceptron and Cerebellar-Model-Articulated-Controller neural networks were used in constructing the driver and obstacle classification models, and were evaluated using a car-following scenario for the driver model and a two-class obstacle (car or pedestrian) for the classification model. In the driver modelling application where the input dimension was low and training samples were rich, the CMAC network was found to achieve better accuracy than the MLP network. On the other hand, in the obstacle classification application where the input dimension was high and training samples were sparse, the MLP network was found to have fewer classification errors than the CMAC network. In both cases, the CMAC network converged significantly faster than the MLP network
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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