905 resultados para Vehicle counting and classification
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Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In this keynote talk we will pinpoint a number of neuroscience problems that can be addressed using Bayesian networks. In neuroanatomy, we will show computer simulation models of dendritic trees and classification of neuron types, both based on morphological features. In neurology, we will present the search for genetic biomarkers in Alzheimer's disease and the prediction of health-related quality of life in Parkinson's disease. Most of these challenging problems posed by neuroscience involve new Bayesian network designs that can cope with multiple class variables, small sample sizes, or labels annotated by several experts.
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Because of the high number of crashes occurring on highways, it is necessary to intensify the search for new tools that help in understanding their causes. This research explores the use of a geographic information system (GIS) for an integrated analysis, taking into account two accident-related factors: design consistency (DC) (based on vehicle speed) and available sight distance (ASD) (based on visibility). Both factors require specific GIS software add-ins, which are explained. Digital terrain models (DTMs), vehicle paths, road centerlines, a speed prediction model, and crash data are integrated in the GIS. The usefulness of this approach has been assessed through a study of more than 500 crashes. From a regularly spaced grid, the terrain (bare ground) has been modeled through a triangulated irregular network (TIN). The length of the roads analyzed is greater than 100 km. Results have shown that DC and ASD could be related to crashes in approximately 4% of cases. In order to illustrate the potential of GIS, two crashes are fully analyzed: a car rollover after running off road on the right side and a rear-end collision of two moving vehicles. Although this procedure uses two software add-ins that are available only for ArcGIS, the study gives a practical demonstration of the suitability of GIS for conducting integrated studies of road safety.
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The main object of this study is to contribute to the study of the train-induced force on pedestrians with a theoretical model based on unsteady potential flow. The same method can be applied to other bodies and other kind of moving vehicles. The outcome of this theoretical model is that the force coefficient (referred to the vehicle speed and the pedestrian cross-section diameter) acting on the pedestrian are proportional to a single parameter which involves the pedestrian cross-section diameter, the vehicle cross-section area and the distance between the pedestrian and the vehicle. The results of the present model concerning the change in modulus and orientation experienced by the pedestrian, as the vehicles pass by, has a similar appearance to that considered in the European standards. The results obtained are mainly qualitative because of the simplifying assumptions needed to obtain a simple formulation leading to analytical results, except in the case of a vehicle with streamlined front shapes, where quantitative results can be expected.
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The database of Clusters of Orthologous Groups of proteins (COGs), which represents an attempt on a phylogenetic classification of the proteins encoded in complete genomes, currently consists of 2791 COGs including 45 350 proteins from 30 genomes of bacteria, archaea and the yeast Saccharomyces cerevisiae (http://www.ncbi.nlm.nih.gov/COG). In addition, a supplement to the COGs is available, in which proteins encoded in the genomes of two multicellular eukaryotes, the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster, and shared with bacteria and/or archaea were included. The new features added to the COG database include information pages with structural and functional details on each COG and literature references, improvements of the COGNITOR program that is used to fit new proteins into the COGs, and classification of genomes and COGs constructed by using principal component analysis.
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Intracellular transport is essential for morphogenesis and functioning of the cell. The kinesin superfamily proteins (KIFs) have been shown to transport membranous organelles and protein complexes in a microtubule- and ATP-dependent manner. More than 30 KIFs have been reported in mice. However, the nomenclature of KIFs has not been clearly established, resulting in various designations and redundant names for a single KIF. Here, we report the identification and classification of all KIFs in mouse and human genome transcripts. Previously unidentified murine KIFs were found by a PCR-based search. The identification of all KIFs was confirmed by a database search of the total human genome. As a result, there are a total of 45 KIFs. The nomenclature of all KIFs is presented. To understand the function of KIFs in intracellular transport in a single tissue, we focused on the brain. The expression of 38 KIFs was detected in brain tissue by Northern blotting or PCR using cDNA. The brain, mainly composed of highly differentiated and polarized cells such as neurons and glia, requires a highly complex intracellular transport system as indicated by the increased number of KIFs for their sophisticated functions. It is becoming increasingly clear that the cell uses a number of KIFs and tightly controls the direction, destination, and velocity of transportation of various important functional molecules, including mRNA. This report will set the foundation of KIF and intracellular transport research.
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In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.
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El artículo aborda de forma crítica y sucinta el recorrido de los estudios sobre historia de la traducción en España, partiendo tanto de los hitos bibliográficos como del recuento de la producción investigadora menor. Ante la dispersión temática existente y el incremento que durante las últimas décadas han experimentado estos estudios, la presente revisión historiográfica pone de manifiesto la necesidad de realizar en futuros trabajos una síntesis periodificadora y clasificadora que pueda ofrecer una perspectiva panorámica y práctica para la investigación y teorización.
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This article is the English version of “Examen crítico de la bibliografía sobre la historia de la traducción en España” by David Pérez Blázquez. It was not published on the print version of MonTI for reasons of space. The online version of MonTI does not suffer from these limitations, and this is our way of promoting plurilingualism.
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This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.
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The master thesis presents methods for intellectual analysis and visualization 3D EKG in order to increase the efficiency of ECG analysis by extracting additional data. Visualization is presented as part of the signal analysis tasks considered imaging techniques and their mathematical description. Have been developed algorithms for calculating and visualizing the signal attributes are described using mathematical methods and tools for mining signal. The model of patterns searching for comparison purposes of accuracy of methods was constructed, problems of a clustering and classification of data are solved, the program of visualization of data is also developed. This approach gives the largest accuracy in a task of the intellectual analysis that is confirmed in this work. Considered visualization and analysis techniques are also applicable to the multi-dimensional signals of a different kind.
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Species distribution models (SDM) predict species occurrence based on statistical relationships with environmental conditions. The R-package biomod2 which includes 10 different SDM techniques and 10 different evaluation methods was used in this study. Macroalgae are the main biomass producers in Potter Cove, King George Island (Isla 25 de Mayo), Antarctica, and they are sensitive to climate change factors such as suspended particulate matter (SPM). Macroalgae presence and absence data were used to test SDMs suitability and, simultaneously, to assess the environmental response of macroalgae as well as to model four scenarios of distribution shifts by varying SPM conditions due to climate change. According to the averaged evaluation scores of Relative Operating Characteristics (ROC) and True scale statistics (TSS) by models, those methods based on a multitude of decision trees such as Random Forest and Classification Tree Analysis, reached the highest predictive power followed by generalized boosted models (GBM) and maximum-entropy approaches (Maxent). The final ensemble model used 135 of 200 calculated models (TSS > 0.7) and identified hard substrate and SPM as the most influencing parameters followed by distance to glacier, total organic carbon (TOC), bathymetry and slope. The climate change scenarios show an invasive reaction of the macroalgae in case of less SPM and a retreat of the macroalgae in case of higher assumed SPM values.
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Mode of access: Internet.
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Mode of access: Internet.
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National Highway Traffic Safety Administration, Washington, D.C.
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Mode of access: Internet.