Instrumentation & Measurement Magazine 23-6 - 16

Generative Adversarial Network (GAN)
GAN is one of the newest members of the DL family, and so
it is not surprising not to see as many applications with GAN
as with the other DL techniques. Among the papers we studied, GAN was used to measure the reliability of transmission
gears [48].

Observations and Future Trends
The following observations can be made from the studied
literature:
◗◗ The spectrum of applications benefiting from DL is quite
broad. Researchers from various fields (industrial and
systems engineering, operations, food control, health
care, machinery, transportation, image processing, circuit
design, sensor networks, etc.) are benefiting from DL
architectures. This ought to drive DL research in I&M
forward and pave the way for promising solutions to
known and/or unresolved issues.
◗◗ Several papers stress that their proposed solutions are the
first to apply DL to their problem of interest. This popularity of using DL is stimulated by the fact that an abundance
of datasets is becoming available for training DL algorithms. In earlier years, many applications suffered from
the scarcity of the available training data.
◗◗ There is a strong focus on using CNN. The reason is that
CNNs are proven to be efficient in computer vision applications [12], so they are the natural first choice in VBM
systems. The fact that rich datasets of images and videos
have been recently made available for training purposes
boosts the usage of AI and of CNN in particular.
◗◗ A considerable percentage of the publications focus on
VBM (around 28%) and on fault/defect diagnosis/detection/prediction (about 25%). Many industry sectors can
highly benefit from such research.
Despite the above encouraging findings, two clear gaps
were observed in the limited literature that we searched:
◗◗ The usage of DL in biometric and security systems: DL can
be an excellent choice to design face recognition, identity
check, or other types of user identification components
for security systems.
◗◗ As Industry 4.0 unfolds, it appears as a major area to
leverage DL. The industrial Internet of Things (IoT) paradigm in particular appears to be a strong candidate for the
utilization of DL architectures. Industrial IoT is heavily
involved in monitoring activities, calibration and control
of sensor nodes, fault detection, etc., which can benefit from the capabilities of DL. Also, given the fact that a
huge amount of data is generated and processed within
an IoT platform, autoencoders can be an efficient tool to
compress data, which reduces the burden on the communication system.

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IEEE Instrumentation & Measurement Magazine	

September 2020


http://people.idsia.ch/~juergen/computer-vision-contests-won-by-gpu-cnns.html http://people.idsia.ch/~juergen/computer-vision-contests-won-by-gpu-cnns.html http://people.idsia.ch/~juergen/computer-vision-contests-won-by-gpu-cnns.html

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