87,407 research outputs found
Fine-Grained Reliability for V2V Communications around Suburban and Urban Intersections
Safe transportation is a key use-case of the 5G/LTE Rel.15+ communications,
where an end-to-end reliability of 0.99999 is expected for a vehicle-to-vehicle
(V2V) transmission distance of 100-200 m. Since communications reliability is
related to road-safety, it is crucial to verify the fulfillment of the
performance, especially for accident-prone areas such as intersections. We
derive closed-form expressions for the V2V transmission reliability near
suburban corners and urban intersections over finite interference regions. The
analysis is based on plausible street configurations, traffic scenarios, and
empirically-supported channel propagation. We show the means by which the
performance metric can serve as a preliminary design tool to meet a target
reliability. We then apply meta distribution concepts to provide a careful
dissection of V2V communications reliability. Contrary to existing work on
infinite roads, when we consider finite road segments for practical deployment,
fine-grained reliability per realization exhibits bimodal behavior. Either
performance for a certain vehicular traffic scenario is very reliable or
extremely unreliable, but nowhere in relatively proximity to the average
performance. In other words, standard SINR-based average performance metrics
are analytically accurate but can be insufficient from a practical viewpoint.
Investigating other safety-critical point process networks at the meta
distribution-level may reveal similar discrepancies.Comment: 27 pages, 6 figures, submitted to IEEE Transactions on Wireless
Communication
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A survey of simulation techniques in commerce and defence
Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches
Fine-Grained vs. Average Reliability for V2V Communications around Intersections
Intersections are critical areas of the transportation infrastructure
associated with 47% of all road accidents. Vehicle-to-vehicle (V2V)
communication has the potential of preventing up to 35% of such serious road
collisions. In fact, under the 5G/LTE Rel.15+ standardization, V2V is a
critical use-case not only for the purpose of enhancing road safety, but also
for enabling traffic efficiency in modern smart cities. Under this anticipated
5G definition, high reliability of 0.99999 is expected for semi-autonomous
vehicles (i.e., driver-in-the-loop). As a consequence, there is a need to
assess the reliability, especially for accident-prone areas, such as
intersections. We unpack traditional average V2V reliability in order to
quantify its related fine-grained V2V reliability. Contrary to existing work on
infinitely large roads, when we consider finite road segments of significance
to practical real-world deployment, fine-grained reliability exhibits bimodal
behavior. Performance for a certain vehicular traffic scenario is either very
reliable or extremely unreliable, but nowhere in relative proximity to the
average performance.Comment: 5 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:1706.1001
Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
Computational complexity has been the bottleneck of applying physically-based
simulations on large urban areas with high spatial resolution for efficient and
systematic flooding analyses and risk assessments. To address this issue of
long computational time, this paper proposes that the prediction of maximum
water depth rasters can be considered as an image-to-image translation problem
where the results are generated from input elevation rasters using the
information learned from data rather than by conducting simulations, which can
significantly accelerate the prediction process. The proposed approach was
implemented by a deep convolutional neural network trained on flood simulation
data of 18 designed hyetographs on three selected catchments. Multiple tests
with both designed and real rainfall events were performed and the results show
that the flood predictions by neural network uses only 0.5 % of time comparing
with physically-based approaches, with promising accuracy and ability of
generalizations. The proposed neural network can also potentially be applied to
different but relevant problems including flood predictions for urban layout
planning
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