2,263 research outputs found
Discrete event simulation and virtual reality use in industry: new opportunities and future trends
This paper reviews the area of combined discrete
event simulation (DES) and virtual reality (VR) use within industry.
While establishing a state of the art for progress in this
area, this paper makes the case for VR DES as the vehicle of choice
for complex data analysis through interactive simulation models,
highlighting both its advantages and current limitations. This paper
reviews active research topics such as VR and DES real-time
integration, communication protocols, system design considerations,
model validation, and applications of VR and DES. While
summarizing future research directions for this technology combination,
the case is made for smart factory adoption of VR DES as
a new platform for scenario testing and decision making. It is put
that in order for VR DES to fully meet the visualization requirements
of both Industry 4.0 and Industrial Internet visions of digital
manufacturing, further research is required in the areas of lower
latency image processing, DES delivery as a service, gesture recognition
for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets
CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor
Autonomous Driving (AD) related features provide new forms of mobility that
are also beneficial for other kind of intelligent and autonomous systems like
robots, smart transportation, and smart industries. For these applications, the
decisions need to be made fast and in real-time. Moreover, in the quest for
electric mobility, this task must follow low power policy, without affecting
much the autonomy of the mean of transport or the robot. These two challenges
can be tackled using the emerging Spiking Neural Networks (SNNs). When deployed
on a specialized neuromorphic hardware, SNNs can achieve high performance with
low latency and low power consumption. In this paper, we use an SNN connected
to an event-based camera for facing one of the key problems for AD, i.e., the
classification between cars and other objects. To consume less power than
traditional frame-based cameras, we use a Dynamic Vision Sensor (DVS). The
experiments are made following an offline supervised learning rule, followed by
mapping the learnt SNN model on the Intel Loihi Neuromorphic Research Chip. Our
best experiment achieves an accuracy on offline implementation of 86%, that
drops to 83% when it is ported onto the Loihi Chip. The Neuromorphic Hardware
implementation has maximum 0.72 ms of latency for every sample, and consumes
only 310 mW. To the best of our knowledge, this work is the first
implementation of an event-based car classifier on a Neuromorphic Chip.Comment: Accepted for publication at IJCNN 202
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