Instrumentation & Measurement Magazine 23-6 - 10

Applied AI in Instrumentation
and Measurement: The Deep
Learning Revolution
Mounib Khanafer and Shervin Shirmohammadi

I

n the last few years, hardly a day goes by that we do not
hear about the latest advancements and improvements
that Artificial Intelligence (AI) has brought to a wide
spectrum of domains: from technology and medicine to science and sociology, and many others. AI is one of the core
enabling components of the fourth industrial revolution that
we are currently witnessing, and the applications of AI are
truly transforming our world and impacting all facets of society, economy, living, working, and technology. The field of
Instrumentation and Measurement (I&M) is no exception,
and has already been impacted by Applied AI. In this article,
we give an overview of Applied AI and its usage in I&M. We
then take a deeper look at the I&M applications of one specific
AI method: Deep Learning (DL), which has recently revolutionized the field of AI. Our survey of DL papers published
in the IEEE Transactions on Instrumentation and Measurement
(IEEE TIM) and IEEE Instrumentation & Measurement Magazine
showed that, since 2017, there is a very strong interest in applying DL methods to I&M, in terms of measurement, calibration,
and other I&M challenges. In particular, of the 32 surveyed papers, 75% were published in 2017 or later, and a remarkable
50% were published in 2019 alone. Considering that 2019 was
not yet finished when we were writing this article, the recent
exponential interest in and impact of DL in I&M is a very evident trend. We also found that although DL is used in a variety
of I&M topics, a considerable portion of DL in I&M focuses on
Vision Based Measurement (VBM) systems (around 28%) and
fault/defect diagnosis/detection/prediction (around 25%).
Finally, we found that Convolutional Neural Networks are the
most widely used DL technique in I&M, especially in VBM.
But to explain all of the above findings, we first need to understand AI itself and what we mean by it in its applied context. So
let us begin our discussion with Applied AI.

Applied Artificial Intelligence
Although the long-term goal of research in AI is to enable
machines to have the same level of intelligence as animals or humans, it is important to note that the Applied AI of today is not
really comparable to biological intelligence. In fact, the word
10	

Intelligence in the Applied AI of today is misleading for the common person, as it gives the person the wrong impression that he
or she is dealing with an intelligent being manifested as AI. This
becomes even more misleading if we consider that we do not
even have a universally agreed-upon definition of intelligence!
In other words, experts do not yet completely understand or
agree what intelligence is. So, then, how can we call something
artificial intelligence if we do not even know what intelligence is?
This has been an ongoing debate among AI experts for a long
time. For brevity, in this article we do not enter this debate, and
we refer the interested readers to other sources such as [1].
So, what does it mean when we talk about Applied AI in
today's context, if it does not mean biological intelligence?
Today, Applied AI practically means the application of AI
methods as tools to advance or improve a system in a given domain; for example using AI methods to improve the weather
forecasting system in meteorology, increase the efficiency of
warehouse logistics in the storage and shipping industry, obtain earlier diagnosis of diseases in medicine, or in the I&M
domain, reduce complexity of a measurement method or increase accuracy of a measurement instrument. Researchers
expect that, in the future, these AI methods will match or even
surpass human intelligence. But today we are neither close
to that goal, nor do we need to be close in the applied arena,
because AI methods as they are today are already having a
beneficial impact on existing systems.
So, what are these methods? Fig. 1 shows some of AI's most
commonly applied methods, which are normally inspired by
how the biological brain works, referred to as computational
intelligence [2]; for example, artificial neural networks try to
emulate the neural networks in a biological brain, or fuzzy logic
tries to operate similar to how humans make decisions without
complicated mathematical modeling and using only imprecise or vague information. What has made Applied AI such a
disruptive technology is the fact that these methods allow us
to do tasks such as classification, clustering, prediction, decision making, and optimization without the need to first build
an analytical model of the problem or the system at hand. This
is very important, so let us take a closer look at this.

IEEE Instrumentation & Measurement Magazine	
1094-6969/20/$25.00©2020IEEE

September 2020



Instrumentation & Measurement Magazine 23-6

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