243 research outputs found
Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information
Accurately and precisely knowing the location of the vehicle is a critical requirement for
safe and successful autonomous driving. Recent studies suggest that error for map-based localization
methods are tightly coupled with the surrounding environment. Considering this relationship, it
is therefore possible to estimate localization error by quantifying the representation and layout of
real-world phenomena. To date, existing work on estimating localization error have been limited
to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D
geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven
map evaluation factors were defined for 2D geographic information in a vector format, and random
forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo.
In the best model, the results show that it is possible to estimate autonomous vehicle localization
error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm
Evaluating the Capability of OpenStreetMap for Estimating Vehicle Localization Error
Accurate localization is an important part of successful autonomous driving. Recent studies suggest that when using map-based localization methods, the representation and layout of real-world phenomena within the prebuilt map is a source of error. To date, the investigations have been limited to 3D point clouds and normal distribution (ND) maps. This paper explores the potential of using OpenStreetMap (OSM) as a proxy to estimate vehicle localization error. Specifically, the experiment uses random forest regression to estimate mean 3D localization error from map matching using LiDAR scans and ND maps. Six map evaluation factors were defined for 2D geographic information in a vector format. Initial results for a 1.2 km path in Shinjuku, Tokyo, show that vehicle localization error can be estimated with 56.3% model prediction accuracy with two existing OSM data layers only. When OSM data quality issues (inconsistency and completeness) were addressed, the model prediction accuracy was improved to 73.1%
FRTRUST: a fuzzy reputation based model for trust management in semantic P2P grids
Grid and peer-to-peer (P2P) networks are two ideal technologies for file
sharing. A P2P grid is a special case of grid networks in which P2P
communications are used for communication between nodes and trust management.
Use of this technology allows creation of a network with greater distribution
and scalability. Semantic grids have appeared as an expansion of grid networks
in which rich resource metadata are revealed and clearly handled. In a semantic
P2P grid, nodes are clustered into different groups based on the semantic
similarities between their services. This paper proposes a reputation model for
trust management in a semantic P2P Grid. We use fuzzy theory, in a trust
overlay network named FR TRUST that models the network structure and the
storage of reputation information. In fact we present a reputation collection
and computation system for semantic P2P Grids. The system uses fuzzy theory to
compute a peer trust level, which can be either: Low, Medium, or High. Our
experimental results demonstrate that FR TRUST combines low (and therefore
desirable) a good computational complexity with high ranking accuracy.Comment: 12 Pages, 10 Figures, 3 Tables, InderScience, International Journal
of Grid and Utility Computin
Incidence, prevalence, and mortality rate of gastrointestinal cancer in Isfahan, Iran: Application of the MIAMOD method
Gastrointestinal cancers remain the most prevalent cancers in many developing countries such as Iran. The aim of this study was to estimate incidence, prevalence and mortality, as well as time trends for gastrointestinal cancers in Isfahan province of Iran for the period 2001 to 2010 and to project these estimates to the year 2020. Estimates were driven by applying the MIAMOD method (a backward calculation approach using mortality and relative survival rates). Mortality data were obtained from the Ministry of Health and the relative survival rate for all gastrointestinal cancers combined was derived from the Eurocare 3 study. Results indicated that there were clear upward trends in age adjusted incidence (males 22.9 to 74.2 and females 14.9 to 44.2), prevalence (males 52.6 to 177.7 and females 38.3 to 111.03), and mortality (males 14.6 to 47.2 and females 9.6 to 28.2) rates per 100,000 for the period of 2001 to 2010 and this upward state would persist for the projected period. For the entire period, the male to female ratio increased slightly for all parameters (incidence rate increased from 1.5 to 1.7, prevalence from 1.4 to 1.6, and mortality from 1.5 to 1.7). In males, totals of 2,179 incident cases, 5,097 prevalent cases and 1,398 mortality cases were predicated to occur during the study period. For females the predicted figures were 1,379, 3,190 and 891, respectively. It was concluded that the upward trend of incidence alongside increase in survival rates would induce a high burden on the health care infrastructure in the province of Isfahan in the future. © 2016, Asian Pacific Journal of Cancer Prevention
Detection of mulberry ripeness stages using deep learning models
Computer Systems, Imagery and Medi
Circadian patterns of Wikipedia editorial activity: A demographic analysis
Wikipedia (WP) as a collaborative, dynamical system of humans is an
appropriate subject of social studies. Each single action of the members of
this society, i.e. editors, is well recorded and accessible. Using the
cumulative data of 34 Wikipedias in different languages, we try to characterize
and find the universalities and differences in temporal activity patterns of
editors. Based on this data, we estimate the geographical distribution of
editors for each WP in the globe. Furthermore we also clarify the differences
among different groups of WPs, which originate in the variance of cultural and
social features of the communities of editors
Clinical and molecular characterization of three patients with Hepatocerebral form of mitochondrial DNA depletion syndrome: A case series
Caps captioning: a modern image captioning approach based on improved capsule network
In image captioning models, the main challenge in describing an image is identifying all the objects by precisely considering the relationships between the objects and producing various captions. Over the past few years, many methods have been proposed, from an attribute-to-attribute comparison approach to handling issues related to semantics and their relationships. Despite the improvements, the existing techniques suffer from inadequate positional and geometrical attributes concepts. The reason is that most of the abovementioned approaches depend on Convolutional Neural Networks (CNNs) for object detection. CNN is notorious for failing to detect equivariance and rotational invariance in objects. Moreover, the pooling layers in CNNs cause valuable information to be lost. Inspired by the recent successful approaches, this paper introduces a novel framework for extracting meaningful descriptions based on a parallelized capsule network that describes the content of images through a high level of understanding of the semantic contents of an image. The main contribution of this paper is proposing a new method that not only overrides the limitations of CNNs but also generates descriptions with a wide variety of words by using Wikipedia. In our framework, capsules focus on the generation of meaningful descriptions with more detailed spatial and geometrical attributes for a given set of images by considering the position of the entities as well as their relationships. Qualitative experiments on the benchmark dataset MS-COCO show that our framework outperforms state-of-the-art image captioning models when describing the semantic content of the images.Algorithms and the Foundations of Software technolog
Dynamics of conflicts in Wikipedia
In this work we study the dynamical features of editorial wars in Wikipedia
(WP). Based on our previously established algorithm, we build up samples of
controversial and peaceful articles and analyze the temporal characteristics of
the activity in these samples. On short time scales, we show that there is a
clear correspondence between conflict and burstiness of activity patterns, and
that memory effects play an important role in controversies. On long time
scales, we identify three distinct developmental patterns for the overall
behavior of the articles. We are able to distinguish cases eventually leading
to consensus from those cases where a compromise is far from achievable.
Finally, we analyze discussion networks and conclude that edit wars are mainly
fought by few editors only.Comment: Supporting information adde
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