31,975 research outputs found
Wireless magnetic sensor network for road traffic monitoring and vehicle classification
Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification
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Using Experiments to Foster Innovation and Improve the Effectiveness of Energy Efficiency Programs
This paper argues that the establishment of a process designed to manage innovation must be developed in California to foster the creation of needed program improvements and develop new and more effective energy efficiency delivery programs. This paper discusses several key institutional problems that must be overcome to achieve significant progress
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A review of miniaturised Non-Destructive Testing technologies for in-situ inspections
Non-destructive testing (NDT) techniques have become attractive trends of product manufacturing, installation and post-maintenance in the aerospace, automotive and manufacturing industry, because of its benefits such as cost saving, easy to use and high efficiency etc. With the industrial products becoming large-scale, high integration and complication, developing the NDT miniaturisation technique for in-situ inspections is highly demanded and becoming an inevitable trend. However, in-situ inspection using NDT have been limited by a number of factors, such as the heavy weight, large size or complex structure etc. This paper aims to systematically identify and analyse the current state-of-the-art of NDT miniaturisation techniques in research and innovation, and discuss the challenge and prospect of miniaturisation of the commonly used NDT techniques
Tiresias: Predicting Security Events Through Deep Learning
With the increased complexity of modern computer attacks, there is a need for
defenders not only to detect malicious activity as it happens, but also to
predict the specific steps that will be taken by an adversary when performing
an attack. However this is still an open research problem, and previous
research in predicting malicious events only looked at binary outcomes (e.g.,
whether an attack would happen or not), but not at the specific steps that an
attacker would undertake. To fill this gap we present Tiresias, a system that
leverages Recurrent Neural Networks (RNNs) to predict future events on a
machine, based on previous observations. We test Tiresias on a dataset of 3.4
billion security events collected from a commercial intrusion prevention
system, and show that our approach is effective in predicting the next event
that will occur on a machine with a precision of up to 0.93. We also show that
the models learned by Tiresias are reasonably stable over time, and provide a
mechanism that can identify sudden drops in precision and trigger a retraining
of the system. Finally, we show that the long-term memory typical of RNNs is
key in performing event prediction, rendering simpler methods not up to the
task
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