Instrumentation & Measurement Magazine 23-6 - 11

contributions to "methods or instruments for
measurement, detection,
tracking, monitoring, characterization, identification,
estimation, or diagnosis of
a physical phenomenon;
uniqueness of an application furthering the I&M
fields; or measurement
theory including uncertainty, calibration, etc." The
popularity of AI in I&M is
therefore not surprising,
considering that AI methods are quite suitable for
detection, tracking, monitoring, characterization,
identification, estimation,
Fig. 1. Some of the most-commonly used Applied AI methods. Deep Learning, the subject of this article, is indicated in
diagnosis, or prediction.
orange. Note that the methods shown in the figure are not exhaustive.
In fact, the huge potential
of using AI in I&M was alMost of these methods take as input a set of previously gath- ready noticed by the 1990s, for example in [3], which describes
ered data and try to come up with a model that best matches this very well neural networks, fuzzy logic, and expert systems,
input data to the desired output. This model can then be used plus their applications in I&M, such as sensor design, calibrato obtain output values for new incoming data. This means that tion, uncertainty prediction, measurement data interpolation,
the model that an AI method creates is purely based on match- software instruments, indirect measurement, fault detection,
ing input to output (black box), unlike the analytical model that and system identification. AI is especially practical when the
a domain expert creates (white box). For example, let us say precise and accurate mathematical modeling of the measurewe want to create a system that detects skin cancer from the ment system or instrument at hand is highly complex, highly
image of a patient's skin lesions. To create such a system ana- nonlinear, highly dynamic, or impossible due to lack of knowllytically, a biomedical engineer needs to develop an analytical edge of the system except for a limited number of parameters.
model based on the skin lesions' shape, pattern, brightness, co- In such cases, or when the accuracy of the final measurement
lour, concentration, area, and many other parameters, some of is more important than understanding exactly how the syswhich are complicated to model, impossible to model, or sim- tem works, AI can offer an attractive and practical solution.
ply unknown. Development of such an analytical model could One example that well illustrates the above notion is Visionbe very complex, especially if the model needs to be generaliz- Based Measurement (VBM) [4], which requires AI as a core
able; i.e., working for any random skin type or color. There are component. An example of a VBM system is [5], which inditoo many parameters here to come up with a general model rectly measures the amount of calories and nutrition in food
that would work with acceptable accuracy. But, if we use an AI from the food's picture. Such a system would simply not be
method, for example a machine learning algorithm, we only implementable without AI, due to the highly complex nature
need to train it with a large-enough dataset of previously taken of the problem and lack of complete knowledge of all paramimages of skin lesions with and without cancer. The method eters. Another area where AI can be applied is calibration, for
then creates its own model of how to match features in those example in [6], where a picture of the user's hand is used to
images to whether or not cancer is present. This not only signif- easily calibrate a complex force measuring instrument. Meaicantly reduces complexity, but also in some cases gives an even surement prediction is another useful feature of AI, which can
better result than an analytical model. Of course, the accuracy be applied when actual measurement is either costly or imof this AI-based model depends on the specific AI method and practical. For example, in a massively multisource networked
algorithm selected, as well as the quality of the provided data- system, it is not possible to explicitly measure the end-to-end
set. With this explanation of Applied AI in mind, let us now take delay between all pairs of nodes on the network, due to the
a look at how we can use it in I&M.
O(N2) complexity of the problem. In such a case, AI has been
shown to predict measurements more accurately and orders of
Applied AI in I&M
magnitude faster than non-AI techniques [7].
Looking at the scope webpage of IEEE Transactions on InstruIn Fig. 1 we can also see Deep Learning (DL), the main submentation & Measurement (IEEE TIM), (http://tim.ieee-ims. ject of this article. Although DL has existed since the 1980s [8],
org), we can say that I&M as a research field is interested in it wasn't until 2011 and 2012 that it caused significant publicity
September 2020

IEEE Instrumentation & Measurement Magazine 11

http://tim.ieee-ims.org http://tim.ieee-ims.org

Instrumentation & Measurement Magazine 23-6

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