36,227 research outputs found
Human response to vibration in residential environments (NANR209), technical report 3 : calculation of vibration exposure
The Technical Report 3 describes the research undertaken to develop a methodology by which human exposure to vibration in residential environments can be calculated. That work has carried out by the University of Salford supported by the Department of environment food and rural affairs (Defra).
The overall aim of the project is to derive exposure-response relationships for human vibration in residential environments. This document in particular focuses on the methods used to calculate vibration exposure from measured vibration signals due to different sources.
The main objective of this report is to describe the different approaches used for calculating the different source-specific exposure. Reported here are findings obtained and a description of the feasibility of the methods used for evaluating exposure for different sources. In addition, an evaluation of the uncertainty related to the exposure calculation is considered
Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach
HTTP based adaptive video streaming has become a popular choice of streaming
due to the reliable transmission and the flexibility offered to adapt to
varying network conditions. However, due to rate adaptation in adaptive
streaming, the quality of the videos at the client keeps varying with time
depending on the end-to-end network conditions. Further, varying network
conditions can lead to the video client running out of playback content
resulting in rebuffering events. These factors affect the user satisfaction and
cause degradation of the user quality of experience (QoE). It is important to
quantify the perceptual QoE of the streaming video users and monitor the same
in a continuous manner so that the QoE degradation can be minimized. However,
the continuous evaluation of QoE is challenging as it is determined by complex
dynamic interactions among the QoE influencing factors. Towards this end, we
present LSTM-QoE, a recurrent neural network based QoE prediction model using a
Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded
LSTM blocks to capture the nonlinearities and the complex temporal dependencies
involved in the time varying QoE. Based on an evaluation over several publicly
available continuous QoE databases, we demonstrate that the LSTM-QoE has the
capability to model the QoE dynamics effectively. We compare the proposed model
with the state-of-the-art QoE prediction models and show that it provides
superior performance across these databases. Further, we discuss the state
space perspective for the LSTM-QoE and show the efficacy of the state space
modeling approaches for QoE prediction
Engage D5.6 Thematic challenge briefing notes (1st and 2nd releases)
Engage identified four thematic challenges to address research topics not contemporaneously (sufficiently) addressed by SESAR. This deliverable serves primarily as a record of the two sets of released thematic challenge briefing notes
Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut using the Cellular Device Network
Epidemiologic studies have established associations between various air
pollutants and adverse health outcomes for adults and children. Due to high
costs of monitoring air pollutant concentrations for subjects enrolled in a
study, statisticians predict exposure concentrations from spatial models that
are developed using concentrations monitored at a few sites. In the absence of
detailed information on when and where subjects move during the study window,
researchers typically assume that the subjects spend their entire day at home,
school or work. This assumption can potentially lead to large exposure
assignment bias. In this study, we aim to determine the distribution of the
exposure assignment bias for an air pollutant (ozone) when subjects are assumed
to be static as compared to accounting for individual mobility. To achieve this
goal, we use cell-phone mobility data on approximately 400,000 users in the
state of Connecticut during a week in July, 2016, in conjunction with an ozone
pollution model, and compare individual ozone exposure assuming static versus
mobile scenarios. Our results show that exposure models not taking mobility
into account often provide poor estimates of individuals commuting into and out
of urban areas: the average 8-hour maximum difference between these estimates
can exceed 80 parts per billion (ppb). However, for most of the population, the
difference in exposure assignment between the two models is small, thereby
validating many current epidemiologic studies focusing on exposure to ozone
Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware
Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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Engage D2.6 Annual combined thematic workshops progress report (series 2)
The preparation, organisation and conclusions from the thematic challenge workshops, two ad hoc technical workshops, a technical session on data and a MET/ENV workshop held in 2019 and 2020 are described. Partly due to Covid-19, two of the 2020 thematic challenge workshops scheduled to take place at the end of 2020 were re-scheduled to January 2021. We also report on the preparation for these two workshops, while the conclusions will be included in the next corresponding deliverable
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